{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tracking Box"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>frame</th>\n",
       "      <th>track_id</th>\n",
       "      <th>type</th>\n",
       "      <th>truncated</th>\n",
       "      <th>occluded</th>\n",
       "      <th>alpha</th>\n",
       "      <th>bbox_left</th>\n",
       "      <th>bbox_top</th>\n",
       "      <th>bbox_right</th>\n",
       "      <th>bbox_bottom</th>\n",
       "      <th>height</th>\n",
       "      <th>width</th>\n",
       "      <th>length</th>\n",
       "      <th>pos_x</th>\n",
       "      <th>pos_y</th>\n",
       "      <th>pos_z</th>\n",
       "      <th>rot_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>DontCare</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>219.310000</td>\n",
       "      <td>188.490000</td>\n",
       "      <td>245.500000</td>\n",
       "      <td>218.560000</td>\n",
       "      <td>-1000.000000</td>\n",
       "      <td>-1000.000000</td>\n",
       "      <td>-1000.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>DontCare</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>47.560000</td>\n",
       "      <td>195.280000</td>\n",
       "      <td>115.480000</td>\n",
       "      <td>221.480000</td>\n",
       "      <td>-1000.000000</td>\n",
       "      <td>-1000.000000</td>\n",
       "      <td>-1000.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Van</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.793451</td>\n",
       "      <td>296.744956</td>\n",
       "      <td>161.752147</td>\n",
       "      <td>455.226042</td>\n",
       "      <td>292.372804</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.823255</td>\n",
       "      <td>4.433886</td>\n",
       "      <td>-4.552284</td>\n",
       "      <td>1.858523</td>\n",
       "      <td>13.410495</td>\n",
       "      <td>-2.115488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Cyclist</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.936993</td>\n",
       "      <td>737.619499</td>\n",
       "      <td>161.531951</td>\n",
       "      <td>931.112229</td>\n",
       "      <td>374.000000</td>\n",
       "      <td>1.739063</td>\n",
       "      <td>0.824591</td>\n",
       "      <td>1.785241</td>\n",
       "      <td>1.640400</td>\n",
       "      <td>1.675660</td>\n",
       "      <td>5.776261</td>\n",
       "      <td>-1.675458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Pedestrian</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.523309</td>\n",
       "      <td>1106.137292</td>\n",
       "      <td>166.576807</td>\n",
       "      <td>1204.470628</td>\n",
       "      <td>323.876144</td>\n",
       "      <td>1.714062</td>\n",
       "      <td>0.767881</td>\n",
       "      <td>0.972283</td>\n",
       "      <td>6.301919</td>\n",
       "      <td>1.652419</td>\n",
       "      <td>8.455685</td>\n",
       "      <td>-1.900245</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   frame  track_id        type  truncated  occluded      alpha    bbox_left  \\\n",
       "0      0        -1    DontCare         -1        -1 -10.000000   219.310000   \n",
       "1      0        -1    DontCare         -1        -1 -10.000000    47.560000   \n",
       "2      0         0         Van          0         0  -1.793451   296.744956   \n",
       "3      0         1     Cyclist          0         0  -1.936993   737.619499   \n",
       "4      0         2  Pedestrian          0         0  -2.523309  1106.137292   \n",
       "\n",
       "     bbox_top   bbox_right  bbox_bottom       height        width  \\\n",
       "0  188.490000   245.500000   218.560000 -1000.000000 -1000.000000   \n",
       "1  195.280000   115.480000   221.480000 -1000.000000 -1000.000000   \n",
       "2  161.752147   455.226042   292.372804     2.000000     1.823255   \n",
       "3  161.531951   931.112229   374.000000     1.739063     0.824591   \n",
       "4  166.576807  1204.470628   323.876144     1.714062     0.767881   \n",
       "\n",
       "        length      pos_x     pos_y      pos_z     rot_y  \n",
       "0 -1000.000000 -10.000000 -1.000000  -1.000000 -1.000000  \n",
       "1 -1000.000000 -10.000000 -1.000000  -1.000000 -1.000000  \n",
       "2     4.433886  -4.552284  1.858523  13.410495 -2.115488  \n",
       "3     1.785241   1.640400  1.675660   5.776261 -1.675458  \n",
       "4     0.972283   6.301919  1.652419   8.455685 -1.900245  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import cv2\n",
    "\n",
    "COLUMN_NAMES = ['frame','track_id','type','truncated','occluded','alpha','bbox_left','bbox_top','bbox_right',\n",
    "                'bbox_bottom','height','width','length','pos_x','pos_y','pos_z','rot_y']\n",
    "\n",
    "df = pd.read_csv('/home/udi-kin/Downloads/kin_dataset/2011_09_26/2011_09_26_drive_0005_sync/label_02/0000.txt',header=None,sep=' ')\n",
    "df.columns = COLUMN_NAMES\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>frame</th>\n",
       "      <th>track_id</th>\n",
       "      <th>type</th>\n",
       "      <th>truncated</th>\n",
       "      <th>occluded</th>\n",
       "      <th>alpha</th>\n",
       "      <th>bbox_left</th>\n",
       "      <th>bbox_top</th>\n",
       "      <th>bbox_right</th>\n",
       "      <th>bbox_bottom</th>\n",
       "      <th>height</th>\n",
       "      <th>width</th>\n",
       "      <th>length</th>\n",
       "      <th>pos_x</th>\n",
       "      <th>pos_y</th>\n",
       "      <th>pos_z</th>\n",
       "      <th>rot_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Car</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>4.433886</td>\n",
       "      <td>-4.552284</td>\n",
       "      <td>1.858523</td>\n",
       "      <td>13.410495</td>\n",
       "      <td>-2.115488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Cyclist</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>737.619499</td>\n",
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       "      <td>374.000000</td>\n",
       "      <td>1.739063</td>\n",
       "      <td>0.824591</td>\n",
       "      <td>1.785241</td>\n",
       "      <td>1.640400</td>\n",
       "      <td>1.675660</td>\n",
       "      <td>5.776261</td>\n",
       "      <td>-1.675458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Pedestrian</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.523309</td>\n",
       "      <td>1106.137292</td>\n",
       "      <td>166.576807</td>\n",
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       "      <td>323.876144</td>\n",
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       "      <td>0.972283</td>\n",
       "      <td>6.301919</td>\n",
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       "      <td>8.455685</td>\n",
       "      <td>-1.900245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Car</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.796862</td>\n",
       "      <td>294.898777</td>\n",
       "      <td>156.024256</td>\n",
       "      <td>452.199718</td>\n",
       "      <td>284.621269</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.823255</td>\n",
       "      <td>4.433886</td>\n",
       "      <td>-4.650955</td>\n",
       "      <td>1.766774</td>\n",
       "      <td>13.581085</td>\n",
       "      <td>-2.121565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cyclist</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.935205</td>\n",
       "      <td>745.017137</td>\n",
       "      <td>156.393157</td>\n",
       "      <td>938.839722</td>\n",
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       "      <td>1.739063</td>\n",
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       "      <td>1.785241</td>\n",
       "      <td>1.700640</td>\n",
       "      <td>1.640419</td>\n",
       "      <td>5.778596</td>\n",
       "      <td>-1.664456</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   frame  track_id        type  truncated  occluded     alpha    bbox_left  \\\n",
       "2      0         0         Car          0         0 -1.793451   296.744956   \n",
       "3      0         1     Cyclist          0         0 -1.936993   737.619499   \n",
       "4      0         2  Pedestrian          0         0 -2.523309  1106.137292   \n",
       "7      1         0         Car          0         0 -1.796862   294.898777   \n",
       "8      1         1     Cyclist          0         0 -1.935205   745.017137   \n",
       "\n",
       "     bbox_top   bbox_right  bbox_bottom    height     width    length  \\\n",
       "2  161.752147   455.226042   292.372804  2.000000  1.823255  4.433886   \n",
       "3  161.531951   931.112229   374.000000  1.739063  0.824591  1.785241   \n",
       "4  166.576807  1204.470628   323.876144  1.714062  0.767881  0.972283   \n",
       "7  156.024256   452.199718   284.621269  2.000000  1.823255  4.433886   \n",
       "8  156.393157   938.839722   374.000000  1.739063  0.824591  1.785241   \n",
       "\n",
       "      pos_x     pos_y      pos_z     rot_y  \n",
       "2 -4.552284  1.858523  13.410495 -2.115488  \n",
       "3  1.640400  1.675660   5.776261 -1.675458  \n",
       "4  6.301919  1.652419   8.455685 -1.900245  \n",
       "7 -4.650955  1.766774  13.581085 -2.121565  \n",
       "8  1.700640  1.640419   5.778596 -1.664456  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.type.isin(['Truck','Van','Tram']),'type'] = 'Car'\n",
    "df = df[df.type.isin(['Car','Pedestrian','Cyclist'])]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "error",
     "evalue": "OpenCV(4.4.0) /tmp/pip-req-build-qacpj5ci/opencv/modules/highgui/src/window.cpp:376: error: (-215:Assertion failed) size.width>0 && size.height>0 in function 'imshow'\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31merror\u001b[0m                                     Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-f6ddbd726b6d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mbottom_right\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbox\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbox\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m#cv2.rectangle(image, top_left,bottom_right,(255,255,0),2)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"image\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwaitKey\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdestoryAllWindows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31merror\u001b[0m: OpenCV(4.4.0) /tmp/pip-req-build-qacpj5ci/opencv/modules/highgui/src/window.cpp:376: error: (-215:Assertion failed) size.width>0 && size.height>0 in function 'imshow'\n"
     ]
    }
   ],
   "source": [
    "box = np.array(df.loc[2,['bbox_left','bbox_top','bbox_right','bbox_bottom']])\n",
    "image = cv2.imread('/home/udi-kin/Downloads/kin_dataset/2011_09_26/2011_09_26_drive_0005_sync/image_02/data/0000000001.png')\n",
    "top_left = int(box[0]),int(box[1])\n",
    "#/home/udi-kin/Downloads/kin_dataset/2011_09_26/2011_09_26_drive_0005_sync/image_02/data\n",
    "bottom_right = int(box[2]),int(box[3])\n",
    "#cv2.rectangle(image, top_left,bottom_right,(255,255,0),2)\n",
    "cv2.imshow(\"image\",image)\n",
    "cv2.waitKey(0)\n",
    "cv2.destoryAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IMU&GPS location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "IMU_COLUMN_NAMES = ['lat','lon','alt','roll','pitch','yaw','vn','ve','vf','vl','vu',\n",
    "                    'ax','ay','az','af','al','au','wx','wy','wz','wf','wl','wu',\n",
    "                    'posacc','velacc','navstat','numsats','posmode','velmode','orimode']\n",
    "\n",
    "def read_imu(path):\n",
    "    df = pd.read_csv(path,header=None,sep=' ')\n",
    "    df.columns = IMU_COLUMN_NAMES\n",
    "    return df\n",
    "def compute_great_circle_distance(lat1,lon1,lat2,lon2):\n",
    "    \"\"\"\n",
    "    Compute the great circle distance from two gps data\n",
    "    Input:   latitudes and longitudes in degree\n",
    "    Output:  distance in meter\n",
    "    \"\"\"\n",
    "    delta_sigma = float(np.sin(lat1*np.pi/180)*np.sin(lat2*np.pi/180)+\n",
    "                        np.cos(lat1*np.pi/180)*np.cos(lat2*np.pi/180)*\n",
    "                        np.cos(lon1*np.pi/180-lon2*np.pi/180))\n",
    "    \n",
    "    return 6371000.0 * np.arccos(np.clip(delta_sigma,-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "prev_imu_data = None\n",
    "gps_distances= []\n",
    "imu_distances = []\n",
    "for frame in range(150):\n",
    "    imu_data = read_imu('/home/udi-kin/Downloads/kin_dataset/2011_09_26/2011_09_26_drive_0005_sync/oxts/data/%010d.txt'%frame)\n",
    "    \n",
    "    if prev_imu_data is not None:\n",
    "        gps_distances += [compute_great_circle_distance(imu_data.lat,imu_data.lon,prev_imu_data.lat,prev_imu_data.lon)]\n",
    "        imu_distances += [0.1*np.linalg.norm(imu_data[['vf','vl']])]\n",
    "    prev_imu_data = imu_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>alt</th>\n",
       "      <th>roll</th>\n",
       "      <th>pitch</th>\n",
       "      <th>yaw</th>\n",
       "      <th>vn</th>\n",
       "      <th>ve</th>\n",
       "      <th>vf</th>\n",
       "      <th>vl</th>\n",
       "      <th>...</th>\n",
       "      <th>wf</th>\n",
       "      <th>wl</th>\n",
       "      <th>wu</th>\n",
       "      <th>posacc</th>\n",
       "      <th>velacc</th>\n",
       "      <th>navstat</th>\n",
       "      <th>numsats</th>\n",
       "      <th>posmode</th>\n",
       "      <th>velmode</th>\n",
       "      <th>orimode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49.011213</td>\n",
       "      <td>8.422885</td>\n",
       "      <td>112.834923</td>\n",
       "      <td>0.022447</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>-1.22191</td>\n",
       "      <td>-3.325632</td>\n",
       "      <td>1.138431</td>\n",
       "      <td>3.514768</td>\n",
       "      <td>0.037625</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.017499</td>\n",
       "      <td>0.021393</td>\n",
       "      <td>0.14563</td>\n",
       "      <td>0.492294</td>\n",
       "      <td>0.068884</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat       lon         alt      roll    pitch      yaw        vn  \\\n",
       "0  49.011213  8.422885  112.834923  0.022447  0.00001 -1.22191 -3.325632   \n",
       "\n",
       "         ve        vf        vl  ...        wf        wl       wu    posacc  \\\n",
       "0  1.138431  3.514768  0.037625  ... -0.017499  0.021393  0.14563  0.492294   \n",
       "\n",
       "     velacc  navstat  numsats  posmode  velmode  orimode  \n",
       "0  0.068884        4       10        4        4        0  \n",
       "\n",
       "[1 rows x 30 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read_imu('/home/udi-kin/Downloads/kin_dataset/2011_09_26/2011_09_26_drive_0005_sync/oxts/data/0000000000.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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UaV/z7mwHoxLICyEQAAAAAACLhLtGPV8eQjvYjJlAkrMhbJgzgWYOn+4kE5uaCVSoWoG2gy0mhEAAAAAAACwSrQ1awxgMXZ4dAo0nI5ooDa8SqFjzNxg6E48oX7Fk27ZTCdSjfWyxIgQCAAAAAGCRmCy57WDDmAnkXGtaO1gyMtztYM2wqld7Vzpuqt6wNVGqqd6wGQzdASEQAAAAAACLxGR5qmWq0bAHula+bMkwpGTbvB6nHawm2x7s2q5iczB0r0HP7oayZycrvh6/WBECAQAAAACwSLgzgWx7qtWqX7mKpXTUVChktL42noyqajVUrjUGurarVHNXxPfYDhZzQ6CypN6VQ4sVIRAAAAAAAIuEuyJemhq63K9CxZrWCiY57WCShtYS5s4u6tXelY0793VDoCQzgTwRAgEAAAAAsEjk2raCuUOi+5WvWNOGQkvSkiGHQKWq03IWj3SPL9Lx6ZVAtIN5IwQCAAAAAGCRmCwPrxIoV7ZmrW4fT0YlSRPF4WwIK1TrSkbCMgyj6+Om2sHcmUAB28GsSl/n290QAgEAAAAAsEjkyjVFTScKGHRDWL5itQYyu6bawYYTAhWrdSV8BDqZ+AAzge74pvT546XJp/s64+6EEAgAAAAAgHmq3rAHrthpN1mqadVYXNIQQqCyVzuYUwk0rHawYtVSysd8n0ysORMo51T0JPy0g9Ut6Sf/T7r6/5OW7idF4gOddXdACAQAAAAAwDz1jRsf1cv/+TdDW7meK1tTIdAIZgKNJZwwZqI0xEqgSO9Ax50JtNWtBOoVHJV2St96k/T7L0gn/LF04ZVSYsmgx5332JkGAAAAAMA89cT2kp6drKhca/irbulhslzTXmMJSVKhOoQQaEY7WDwSViIS1o7CsAZD12fNHfISDhlKRsPamvMxE+j5h6Rvny/teFR6/WelY98+lLPuDgiBAAAAAACYp0o1Z0V6rlwbOASqWg2Vaw2tGo83r9l/CGTbtmclkORsCBvWTKBC1fseXjJxU8XWYOgOr9VDv5Quf4cUjkhvv0ba50VDOefugnYwAAAAAADmqVKzWmdywNYtaWo9/B7pmMyQMdCsoWK1LtuWZ0AzloxqojS8SiA/7WDS1Fmi4ZAi4Rlxh21Lv/8P6dJzpPG10nt+vegCIIlKIAAAAAAA5i23EmiyPHhljVv5M5aIKB03BxoM7T53ZjuYNPxKID/tYJKUiTvziJJe84Bu/IL0s7+QDj1dOus/pVh6KOfb3VAJBAAAAADAPFWsuu1gg1cCuUFSJh5RKjpYCOSex7sdLDq07WClat13G5y7Jj45s3LowZ9JP/9L6bAzpDddsmgDIIkQCAAAAACAeatUnZoJNCg3uMnGTWXi5kDbwdwAKeNRCTSWjGhiWJVAlbpSQUOg9mBq6ybpyndJK4+QzvqiFFrcMcji/ukBAAAAAJjHWu1gpSFUApXaKoFi5kDbwdx5QulYZNb3liQj2lmqDbzWvtGwVarVlei26auNW5XUCo2K250tYGZcuuDbUjQ10HkWAkIgAAAAAADmqWFWArntYNmEqXRssEogt6oo5TF/Z0kyqnrDHniYtRuA+a0EcgOpZNSU6jXpu2+TJp+Szv+WNLZmoLMsFIRAAAAAAADMU1Mr4oexHcxt4Yo4IdAQBkNnPCqBxhLO1wZtCXPnIXVc9z5Dqx0sGpZ+/BHp0eulMz4rrT1+oHMsJIRAAAAAAADMU24QMoztYJOlmgxDysTMwUOg5nm8t4NFJWng4dClVgjkdzuY87hXF38g3foV6aT/LW04f6AzLDSEQAAAAAAAzFPDrASaLFtKR02FQoYzE6hS7/taboDk2Q6WciqBBg2B3JlFQSqBXhTaqLO3fk46+DXSKz420P0XIkIgAAAAAADmoXrDVtVqSJoa6jyIyXJN2WarVjruVAI1Gv0Nb85VLEXNkGLm7IBmLOFUAk0MeGa3CsrvivgVtaf175F/0/PxfaSzvyyF/D1vMSEEAgAAAABgHnKrgKThzQRyW6bSzQqeYq2/aqBCxVIm5t2mtSTZrAQqDFYJVKy61UY+2sGqBR1/8/slSdcc9s9SLDPQvRcqQiAAAAAAAOahYtsK92HNBMrGm5VAzYHO/W4Iy5etjuGMOxh6x5AGQyciPSp6bFu6+n1KTfxB76+9X7WxvQe670JGCAQAAAAAwJDc8NDz08KbQZSrTitYOGQMvRLIneXT73DofMVSukMIZIZDysTNIbSD+awEuvHz0r3f07YT/q+ubxypZK/QaBEjBAIAAAAAYAi2F6q68Cs36Xu3PzmU6xVrTgiyPB0bTiVQ20wgNwzqNwTKlS3PzWCuJcnowIOhfa2If/g66ecfkw47Q9GX/B+NJSI6YEV6oPsuZP72rAEAAAAAgK52Fquyben5fGUo13NDkJXZmO5+sqxGw1YoZPR9vVzZUtatBGquXS8MUAm0Zzbe8ftLkpGB28FKvUKgnY9LV7xD2uNg6Q3/rmwsqrs+/qqB7rnQUQkEAAAAAMAQuCvXdw4YfrjKzRBkeSYu25byA7SZNRq2cuWaMvGp7WBS/wOnC5XulUBjyagmBl0RX3FDII/71ErSZW+R6jXpvEsZBO0TIRAAAAAAAEPgtlYNOgvH1V4JJA22IaxQtdSwpWzC3Q42eCVQp5lA0nAqgYo1Zw19eGb1k21LP/gz6em7pDd+WdrjwIHus5gQAgEAAAAAMARuoLJzwAoYl7sifmWz7So3wFwgN0BqVQLFhjATqGsINISZQJW6Ul6tYLf8l3TXt6SXXCwdctpA91hsCIEAAAAAABgCN1DZOaRKoNKMSqDJUv+VQO5gaXdFfGqAEKhqNVSxGl1DoLFERLmyJave6OO0jmK1PrsV7MnbpJ/8uXTwadJLPtL3tRcrQiAAAAAAAIZg2O1gbiXQiqFWAjmhSswMKRI2+gqB3Iqn7tvBnLBpkNeiWLWmD4UuT0hXvFPK7Cmd9UUpRKQRFK8YAAAAAABD4IYjE0MaDN2aCZRxQ6ABKoGaYYy7It4wDKViZl8zgdzgqGs7WCoqSQPNBXIqgZohkG1L1/5vaecT0tlfkRJL+r7uYkYIBAAAAADAEBTa2sFs2x74em4l0B4ZJ1CZHGIlkOSEOPk+giU3BMp02w6WcCuB+p8L5FQCNe9x+zeke78nvfwvpL1P6Puaix0hEAAAAAAAQ5BvrjSvN+y+By63K1UtJSLh1hyfgSqBZswEkpoh0ECVQJGOj1mSbFYCFYZQCbT1funHH5H2f6l00p/2fT0QAgEAAAAAMBTtrVU7h9ASVqo5IUg8ElbUDLVauvrRsRKonxCoea1UzGNzV1MrBBpgQ1ipWtdYxJIuf4cUS0tnfYk5QAPi1QMAAAAAYAjaA5VhDIcuVuuKR5ygJRuPaHLAmUBRM9S6nqS+ZwLl/LSDDWEwdKFq6bznvyA9d7/0xi9JmZV9XwsOQiAAAAAAAIZg2CFQqW0wcjZuDrQdbLJsKTsjtEnHzVagE4RbCdStHSwbNxUOGQNVAr24er1O2HGtdPKfSge8vO/rYAohEAAAAAAAQ1CoWNoj7bRBDbMdTHKqbgaqBCrXps0DkqR0tN/tYM7P1m1FvGEYGk9E+t4OZm9/RB+3/1NPptdLL/uLvq6B2QiBAAAAAAAYgnzF0urxhCRp5wBbsVzT2sESkYEqgXJlS5nEjBAo3u92sLoMQ0pGOs8EkpyWsIl+QiCrIvvyd6ghQ79c97dSuHPFEYIhBAIAAAAALFhbc2X9+oGtu+Rehaql1UuaIdAQKoHKMyqBBtoOVqrNbgeLmSpU62o0gq2zz5ctpaKmQiGj6+OWJKP9tYP95M8VevoOfbj2v1TPrg3+fHRECAQAAAAAWLC+eeNj+qOv36p6wKCjH/mypWWpmGJmaGiDoRNuCBSLDLQdzLMdLOaEQoVqsHApX6m1ntvNkmQf7WB3XSbd+hXljn2vftY4vhWCYTgIgQAAAAAAC9ZEqaZ6w1apVh/5vQqVulIxU+P9tkHNUKrWlYg4YUs2MVglUK5szdrm5c70KVSCvTb5itV1HpBrLBHVRJBKoGfvk679oLTPSXr2+P8rSUpGe98H/hECAQAAAAAWrHwz4Cj2MQA5iKrVULXeUDoW1ngiOpSZQKVaXYmo87E9E4+oVKurVm/0da3JUk3ZGTOBUs1qHnfQs1+5sjX8SqDypPTdt0qxjHTOf6tQc1rNqAQaLkIgAAAAAMCC5W6/KlZHWwnk3icVMzWWjAxnO1i13qqEcat4+qkGqlh1VayGMjOCm0wrBApeCTSzqsjLklRUpVpd5V5VWLYtXfM+afsj0rlfkzJ7tt6vBCHQUBECAQAAAAAWLHfeTdC5N0Hl20Kg8URk4JlAjWYLW2s7WHOeTz8bwtzgqGMlUMBgqVDxVwk01rxfz9fi9/8h3Xe1dOrHpX1PkiQVm+9XinawoSIEAgAAAAAsWG44Uxp1JVAztEjHTI0lBq8EqlhO21f7djCpv0og9zmzZgK1KoECDoYuW60AqZslyagkdd8Q9vjvpZ//pXTo6dKLPtD6slsJRDvYcBECAQAAAAAWLLdNqzDiEMitpkm7g6EHrARyK2ESbiVQs6qmnw1h7nM6bQcLGgLlfFYCLUk699tR6HDm/HPS5RdJY2ulM78gGVMr592fP+njPvCPEAgAAAAAsGC5m69Ku7IdLOlzFk4XM2fiuFU8k8OsBGptB/N/Tdu2fc8EGku67WAelUBW1QmASjuk8y6REuPTvt2qBIpQCTRMhEAAAAAAgAXLDWeCrkEPyr2+2w4m9Ve143IDJLcdapCZQJPN58yeCeRcO0glULFal23LZyWQ2w4248y2Lf3wT6XHfied8Xlpz/We95GkZIwQaJgIgQAAAAAAC5Jt21PbwQaoyvFjajtYWOPNCpidA4RArUqgGYOh+6sE8g6BYmZY0XAoUAjkPjbtZztYp5lAN3xWuuOb0in/VzryXM/nFquWwiFD0TCxxTDxagIAAAAAFqSK1ZDVsCVJxYBzb4JqhSNtlUCDDIcu1aa3g6Vbg6H7mQnk3Q4mOaFVkO1g7T9nL/FISFEzpIn212HTD6Wff1xad5b00j/v+Nxita5kJCyjbU4QBkcIBAAAAABYkNpn3RRHPRh62op4pwJmkOHQpRmVQOGQoVQ03Ap0gsiVazIMKe2xbj0dNwPNBGofgN2LYRhakoxMVQI9fZd05R9Jq4+R3vAfUqhzJFGs1GkFGwFCIAAAAADAgtQ+B6g44sHQhYqlmBlSJByaagfrthq9h1JrJtBU2JJNRPqcCeRs8wqFZlfVpKKmcv20g/nc2rUkGXVmAuWekb59gZRYIp3/LSmS6Pq8Yq0+7WfHcPCKAgAAAAAWpPwurgRyg5GprVjDmwkkOe1cuT5mAk2WarPWw7dfM0g7mHt/PzOBJGksEVGpkHMCoNJO6Z0/kTJ79nxesWK1hmJjeAiBAAAAAAALUqG660KgQsVSqhkCZWKmwiFjqDOBJCkTj7Q2fQUxWe680j0VM7W94L9iyQ3WMjHvUGmmpQlTFzz+j5J1h1MBtOpIX88rVuuEQCNAOxgAAACAReehrTlty1cCP++WR7er0Rw0jNG484mdrfXog5peCTTqwdD1VghkGIbGEhHtLA3QDtY8b3sIlO23Eqhcm7UZzJWOBasEyjdDKL+VQOflvqpTrBukV35COvS1vu9TrFq0g40AIRAAAACAReeir96if/vlHwI956GtOZ37xRv16we2juhUmCzXdPZ/3KDLb9sylOu11rZHw7ukEijdNsh4LBEZrBKo2pA0sx2sv5lAubKlbIfQJh0zA62ILzRfx5Sfoc3/81m99LlLdWn9FXr2iHf7vkfFquvx7UUtS0V9Pwf+EAIBAAAAWHS25avalg9WpfF88/Fbc8EriODPRLGmesPWc5PloVzPDYFWZOO7dCaQ5IRAA80EqlmKmiGF24Y5Z+KmJoc8EyhoCJQrW4qGQ4qZPUKg2y+Rfv6XKhx0hj5mvUPfvuUJ3/f48T3PaEexpjccvdr3c+APIRAAAACARaXesFWq1QN98JWmAoVBPtiju/yQX+N8czvY8nQs0Br0frTPBJKk8eRgIVDJYyaOux3MtoO1JObKta4zgYrVuuo+2xzzlVrvVrD7rpGu/YB0wCuUOu8revHBK3XpTY+rajV83ePrNz6q/fdI6eQD9/D1ePhHCAQAAABgUXFnwwSdEeO2wQzS4oPu3Pdk55BCIDf4WZ6JtQYtj8rMSqDxgdvB6kpGpodAmbipWt1WxWeYIkmNhq1cxeo4E8gNhwo+/z7ky1b39fAP/0a68l3S6uOk8y6RzKjefuK+ei5X0U/vfabn9e/ZMqE7Ht+pt564j+dKewyGEAgAAADAolJoVoe4VSL+n0cl0Ki578mwXuNCxWmpyiYirfd9VGZXAkW1s9j/YOhira74zEqgZkvXZIDXp1C1ZNvqWgkkyXel1Mywa5onb5O+c6G07EDpzZdJ0ZQk6SUHL9feS5O65MbHel7/Gzc+qmQ0rLOPXePrPAiGEAgAAADAouK2HAVtD5oKgfr/YI/u3Nd4WNVWbmCRjIZb27ZGodGwVajWp4VAY4mIJsuW7zarmcoe7WBukBNkLpD72G4zgST53hCWK1ve7WDPPSB98xwpuUx66/el5NLWt0IhQ2994T66+dHtuv/pyY7X3lGo6pq7ntJZR6/ueF4MhhAIAAAAwKJS6DMEGva8GszmvsZBKl26capzwkpGwyrW6oFn6fi+TzNgyswIgST1tc1LkorV+rTNYNJUkBPkmu5ju62Il+R7Rlah6lEJtPMJ6RtvkMIR6W1XSZk9Zz3v3OPWKGaG9I0u1UDfvfUJVayG3nbivr7OguAIgQAAAAAsKm74w2Do+WfYr3G+UlcqaioZNWXbUrnmf5ZOEG6r2czB0FL/VU2lWl2J6PSwJZvooxKo1AyoOq2IjwcLgWbNBKrkpW9fIFXz0lu+Jy3d3/N548mo3nDUal11x5Oa8HhN6g1bl/z+MZ2w31IdsmfG11kQHCEQAAAAgEXF/bBbsRqy6v5DAXdeDYOhR6fVDlYKvgGr0/XcdjDJ//DjoNzfqVRsqnKnFQL1GWiVqnUlItM/smcGqQTq0F6VivYxE8gNlBoN6fv/S9p6r3TOV6U9j+j63LeeuI9Ktbouv232uvjfPLBVW3aUqAIaMUIgAAAAAItKsTo1ILhQ9T8s2N1cRSXQ6LhBW705Y2dQhaozrNkNgUpDuKbnfZoBSnpaO1hUkvoeDl2q1ZWcUQnkVvPkAs0Eqk177kxBr5krW1Ntb7/5W2nTD6RXfUo66NSezz1i9ZiO3WeJvvn7x9SYMSvp6zc+ppXZmF61bqWvc6A/hEAAAAAAFpX2tpcgc4Hcx+YGGPaL7optlTrDCNumBkMHW4MeVKFVCTS7Hazfn6NYrSveYSZQkJlJbjtYp5lAQbaDVa2GKlbDCbs2Xin99p+ko98qvfBPfJ/nbSfuo0e3FfXbPzzX+tojzxf02wef05tfsI8iYWKKUeLVBQAAALCoFPoMgdrDo2ENLsZ07a/xIOvVXa3B0M02reKIKoFynpVAg80EKtdmbwdLRsMKh4xAlUC5HpVAbgubn5lA7t+XfSoPSFe9V9r7RdLrPiMZhu/zvOaIVdojHZu2Lv6SGx9TJGzoghPW+r4O+kMIBAAAAGBRaQ9+ggyHdof/SrSEjUr7ezOM17hQcda2J5sVNcXKrmwH678SyLZtFavWrO1ghmEoHTNbLV5+TJYtxcyQYmbY8/sxM6xoONRqxesmX7G0Qjt06l1/KqVWSOddIplR32eRpKgZ0ptfsFa/emCrHt9WVLFq6fLbntBpR6zSikw80LUQHCEQAAAAgEWl/cNuIUAoUKhMfSgnBBqNQmVqLfqg1Va2bbfWmbstT8Vd2A4WCYeUjpl9VQJVrIYatpSIzg5usgkzcCVQpsNQaFc6bipf6X3OQiGnL0X/WdF6Xnrzd6TUHr7P0e7NJ+yjkGHomzc9pqvueEq5sqW3n7hPX9dCMIRAAAAAABaVfiuB8hVLq5ckJPW/8QndTXuNB9zCVqzWZdtOMOOGKaNqB3ODxWmr0+VUA+0sBW9rK9ec682sBJKkTCwSaDvYZMlqrZbvJBUL9w5EbVvLf/UhHWk8ogdO+oy0cp3vM8y051hcr163Upfd8oS+dsMjOmxVVsfus6Tv68E/QiAAAAAAi0q+askMOTNMgg6G3mvcCSioBBqNYb7G7dU57hr0UYVAhYqlkCHFZ6x0H09GNNFHmOWec+ZMIMmpBHKHPfsx6acSKBbpXV30m7/Xskeu0aetN6l6wGm+79/J207cVxOlmh58Nq+3n7iPjABzhdA/QiAAAAAAi0qxYmlFJub8s8/2oEbDVrFW1+pxZ2bJxBCGFvfy4cvv0l98/56R32c+KVbrWp6OyQwZA1db5VtzesJtlUCjaQfLV5xV9DODjPFkpK+fo+RWAnmEQJl4JPBMoGyHodCudCzcPRC945vSdX+vx/d+o/69fkbHIdNBnLDfUh2yMqNs3NSZR60e+HrwZ/B3DgAAAAB2I4VKXSuycT01UfY1DFdyPpTbtrRqbNdVAm18alIxc3H9/+2dle5hjSUiQ6gEct7bVNRsVdSMrh3MmtUKJjntYM9O5gNfr1Tt0g4WDz4TaE2zxa6TdMzU8/kOweZDv5Su/aB0wMv1Pwd+VHpwk9Kx7pVFfhiGoc+/+WhNli3PsAujsbj+iwIAAABg0ctXLC1LRRUy/LeDuY9bkooqEQnvkhAoV64Fmv2yu7Ntu7nS3dRYcvAQKN+2sSsSDikaDqkwwsHQ3iFQtK/ZRm4lUDI6+5rZoJVApd6VQKmY6f134em7pe++TVp+mHTu15WvGc3HDye0OWhlhllAuxghEAAAAIBFpVC1WnNi/A6Gbm8tGktEBh5a7PeeQQZX7+4qVkNWw3ZCoER/s3TazdzYlYyFWxU2w+a2g800noxoolSVbduBrudWLCWisz+yZ+PO722j4e+auXJN2R4zgTJxU7mZv2sTW6RvvUmKj0kXfleKZ1uPSXmEU9g9EAIBAAAAWFTcapOO1Q+ez5lqLRpGq1Ivtm0rV7YCtf3s7gptlTtDaQerzgiBIj42YPV7rw6VQOOJiGp1O3Ab2lQ72OxrZuIR2bZ8VTVVrLoqVqPnDJ9UdMbfhdJO6ZvnSNWCdOEVUnYvSVK+7PycoRBDnHdXhEAAAAAAFhV37kwqFvbdHtTeWjSMVqVeSrW66g0nPKj7rPjY3bWCtpip8SGEQO575gYgyZipUm1U7WB1zxap8aRTgRN0OLR7Tq9ZOe6690kfAaEbImYTPbaDxc2p3zWrKl32FmnbQ9J535RWHt56XL5S8wy7sPsgBAIAAACwaNQbtsq1hlIxU+mY6XswdHtr0a6oBMq3fcDPL5JqoNktd4NtYJvVDhYdXSVQp3awsWb4EvRn6bYi3l337mde1GTz97RXJZAb7BQqVema90mPXi+d+QVp/5dMe1y+Yik9hM1gmDuEQAAAAAAWDbfyJ91sByv6bQer7toQqL3KI8gQ4N1Zsf01TkaVq1gDVUG5AV+yuWErGR3tTKBMh8HQUvBtcu454x22g0ny1Sro/h71mgmUjpkKqaHwDz4o3X2Z9PKPShvOm/W4XNm77Q27D0IgAAAAAItGe3VIKuZ/MPRUq1J4KK1KvbSfa7EMh3Z/zmRz7pJt+6t26aRQsZSKhlvza5JRcyTbwdq3ms3ktoMFHXJd6lIJ5AY6kz5+B93XL9MjBEpFDf1T5ItK3ftt6SUXSy/+kOfjOs0+wu6DEAgAAADAotEeAqVj/kOBme1gxWpdVasxsnO2hx+LZTi0G7S5g6Gl4BU00683PZgZVSVQ+1azmfqfCVSXGTIUCc/+yB6oEqjkzgTqEtzUa3rB7R/R2eHf6aljPiS97M8lw3vwc54QaLdHCAQAAABg0ci3tnyFA82Iybetxm5Vd4ywGmjaTKDK4mgHmwranGorabDXeGZgkYz6HwQeRPtWs5nGm+1gOwNWAhWrdc+h0NJUVY+fNsGelUBWVbrinVr5+A/1t7ULtPmwP+56vXyZmUC7O0IgAAAAAIvGzEog/+1glhKRsMIho7VpaaI02ODibtqrPBZLJdDMDWxS8PCk3exKIDPwqnZ/95naajZTPBJS1AxpZ8DflXKt7tkKJgWdCeS8flmv4MaqSJe/Xbr/Gj3zor/Sl+qv7zmEPEcl0G6PEAgAAADAotFetZGKmapaDdXqvdu6CtWpQGEYrUq9tFd5+FkFvhDMbLmTBm0Hm762PRkNq1ity7b7HzbtJdes1Ep7rIg3DENjiYiv+T3titW6Eh5DoSVnWHTUDPmsBLJkGE4F2zS1kvSdC6UHfiS99tOqHfe/JHWfP2XbtjMAm0qg3RohEAAAAIBFo33LlxvqFH20hBUq9daH/PFkfxufgpg2GHqRhED5qqWoGVIkHBpJO1gqZqresFX1EfoFMTXLyLvlajwRCVzRVKrVlZgZ3LTJxiOteT/dTJZqysTM1nBsSVKtLH37AumhX0iv/6z0gne3XqduIZAToHm3vWH3QQgEAAAAYNHIt235ckOdvI85MYWKpWR0eiXQIK1KveTKlpLRsMyQMdCGrN1JsVJXqtkClR1GJVB1ejuYW1njJ/QLdJ+2WUZexpN9hEDVuhKRzh/Xs3HT1+9Frmy1XktJzgyg775Vevg30plfkI59e/PszutU6BICtVdqYfflKwQyDOM0wzAeMAzjIcMwLu7wmDcZhnGfYRj3GobxreEeEwAAAAAGN7MdrP1r3bRXleyKdrB82Wm7ycT9zy3a3bXP8IlHwopHQkPdDuaGNMXacEOgfJfB0JI0loj2tR0s2aUSKBM3fc8Eag2FrtekK94h/eFn0un/Ih19YetxUdOZXZTr8rvmfo92sN1bzxDIMIywpC9Ieo2kwyVdYBjG4TMec5CkP5d0km3b6yT97+EfFQAAAMBCVa7VdcnvH1OjMdx5LTMVKs6MlEQk3JqT4idkcapKmlUqzQ/BowyBcpWa0jFTaZ8f9v1oNGxdcuOjKg8xBNn0zKSue/C5oVxrZvvWWCKincX+h2/PvJ7bXlUccqjWq0JmPBnRRMCfo1itK95hJpDkVEr5mQk0Wbac39dGXfr+/5I2/UA67R+k494x67HpmNk1EHXbEmkH2735qQR6gaSHbNt+2LbtqqTvSDpzxmPeLekLtm3vkCTbtrcO95gAAAAAFrJfb9qqv7xqo+59anKk98lXLKWipgzDCFQJ5AwZdh5vhkPKxMyRt4Nl4hFlYpGhtYPd8+SE/vLqe4cW2kjSF369Wf/ve/cM5Voz27fGEpG+gzar3lC51pg2ENltNRv2hrB8jxCon5+jVLU6bgeTAlQClWrKxsLSNe+XNl4pnfpX0gu918CnY2bX+VNPT5QkSXukYz3vi/nLTwi0WtITbf++pfm1dgdLOtgwjP8xDOP3hmGc5nUhwzDeYxjGrYZh3Prcc8P7Dw8AAACA3ZvbarJjgMoPP5wWIefDtfu/QdvBpGYlxigrgdrawYZVCeQGEcM8985idWjvWb4taJOk8US07xCoUJ2a/eRKNEOVgo8ZUEH0agcbT0RUqNZVtfwPpC7VOm8Hk+Q7HMyVarpo4vPSnZdKL7lYOvlPOz42FTNbM7O83L1lQmbI0CF7ZnreF/PXsAZDm5IOkvRSSRdI+rJhGOMzH2Tb9pds2z7Otu3jli9fPqRbAwAAANjduUGMnxaXge5TnQoapjYi+dkONr1KZTwZCTznJYhcuTb0EMgNK4Y5Y2iyVFOxWldtCBu3ChVr2pr1bB9btdqvJU0PZtyqoNKQK4EKFUuJSFjh9g1cbcaTwWdIFav1VmjlJZswe28Hs229p/LfOmnH1dJJH5Re6jnetyUTM5WvdD7jPU9O6JA9M13b1DD/+QmBnpS0tu3f1zS/1m6LpGts267Ztv2IpAflhEIAAAAA0JPbojPKOTuSGzQ4YUBrRXyPypBGw1axWh9aq5If+YqlTCyiTDwytNDGrRwZVqgkDbe6qNBs1XONDVBt5TWnJ9mqBBp2O1i968assWRUkjRR8l8xVa51D4Ey8YhKtS7hW70m+6d/obfrB7p91XnSqX8tGd4hlSsVC7fW3c9k27bu3jKhI9eM+f4ZMD/5CYFukXSQYRj7GYYRlXS+pGtmPOYqOVVAMgxjDzntYQ8P75gAAAAAFjI36NgVIZAbNExVAnUPRdxtUqm2D+WjDoFyZUvpuKl0zN8qcL/XlIZbCeS+BsN4Lbyqrfq9rleLVjLmVgINfzB0usN6eMlpB5Pku6qpVm+oVreV7NYO1hxO7jnD5/mHpK+8Ssbvv6CvW6/UbYd+pGcAJEnpLoHjE9tLmijVtH71uK+fAfNXzxDItm1L0vsk/VTS/ZK+a9v2vYZhfMIwjDOaD/uppG2GYdwn6deSPmzb9rZRHRoAAADAwlLcRSFQe9VGzAwpHDJ6zgTyqioZT/bfqtSLVW+oWK1Pawez7cG3prkf8IdVCWTbtibLw3nfbNtutupND9oKfbaauRUt0yqBIuFp3xuWmeHVTG47mN/fl1IzdOzaDtZc+z6tfdK2pVu+Iv3ni6XtD2vba7+kj1vvULZ5/17SsXDHEOjuJ3dKEpVAC4Cv3W62bf9I0o9mfO1jbf9sS/qz5h8AAAAACMSdyzPKYcvS9MHQhmEoGe3cAjN1ttlVJe5gaNu2Zfiosgh2Ruc8mXhEVashq2GrYjUGnsXihj/DqizKVyzVG044NWgIVLEaqjfsWUGbe+2gG6mmNnZNvWbJ5j+7Icuw5HqEQGOJYDOB3JlF3dvBnPu1Ar38Vunq90l/+Km0/8ukN/y7thbSkq5XJu43BOq8HeyeLROKhkM6eCVDoXd3wxoMDQAAAAB9c+fy7JJ2sLYP7OmY2bM9yrMSKBFVtbmGfNjc6o5MzFS6+WF/GAOz80NuB2t/rwZ937yCtqDhSTuvwdDRsL/Kr37ulelWCZRwZgL5HSTuhkDdV8S3VQJt+qH07y+UHrlOOu0fpLd8T8ru1QpUsz5DoFTMVKlWbwV77e7aslOH7ZVV1CRC2N3xDgIAAACYc7tsJlB1+qr3VMzsGQp4VZW4AcXOAMN+/XLvl4mbynab/RJQrjLcwdDt79WgFVytoC06vdpK8t9GNe161dnBnVv5VRzBdrBulUCZuCnDkCaK/n5X3PN1WxGfTZiKyNLqG/5S+s6bpexe0nuuk174x1LI+Zjvvs9u1VAvnWZkNRq2Nj45qSNX0wq2EPj7bQAAAACAEdoV28GsZuVOe9CQ8lUJ5JytU5XKqrHEUM/pfnhPx01Vrca0rw3jusMIlKTRVAJNr7ZqVrv0cW2vyiJJzRBouJVAvbaDhUKGxhIR/5VArZlAXaqLGhO6JPp32mfz/dKJ75Ne8XHJjE57jFs95oZpvbhhUaFitX6/JemRbQXlK5bWMw9oQSAEAgAAADDnCrugEshdDd5e0ZOO9a4MKXpUlbTm1YxgOLQ7s8edCSQNp4XLvcaw2sEmhxgCue+BV9DWT7VVoWIpHDIUm9G+lIqaI6kE6rYdTHICLd+DoXtVAj2zUasuv0B7GE/rN+s+pZe++n2eD3NDv6zPSqBUh0qge7ZMSGIo9EJBOxgAAACAOee274wiVGndw6M6JBUN0A4W9Qoohn/e9nawqQHAg9/HDQWGMV9Imgp+QsbwKoGSHi13/fxO5MuWUtHwrKHdiSG3g1n1hkq17pVAkvOz+H2N3NDRcybQ/dc6698bNZ1b/ZjuXPKqjtdxQ7ogg6Gl2SHQ3VsmFI+EdODytK/rYH4jBAIAAAAw59yWq1zFUsNjMO1w7jG7oifYYGiPgGIEIZC7dj0TM1sfzCeH0MLVPhh6GCvn3Z991Vhi4NfBK6Cbeo2D/+z5Sn1WK5jkVgINrx2s4FHB5GUsGQ3cDjZtG5xtS9f9o3TZW6QVh8p496+1OXJw1zbBXMVSPBLyPcy5FQLNuOY9T+7Uur3GZIaJDxYC3kUAAAAAc65QsRQNh2TbwxtcPFOnteG9K4GabWTtlUAjbAfLtwb6RlqbnYYyGLpck2E4eUJhCNUwE6WaQoa0aiw+tBCoPaAzwyGlY2bf7WBe1TnDrgTyCq+8jCcivgdDz9oOVi1Il18k/fpT0pHnSxf9SMquUjYR6TovabJU810FJKm1ia7974NVb2jjk5Naz1DoBYMQCAAAAMCcsuoNVayGVo3HJQ2vXWmmgkeY42wH6x4KFCqWktGwQqGp1qJ01BxKG5SXXLmmcMhQPBJqBVaDBmP1hq1Cta4VmZik4YRKkyVL2URE48lIX9U67dygLT1jGHKQNqp2hap3CJTyMQMq0H08wisv48k+BkNHwlJph/SNM6X7rpZe+QnprC9KEefvSSZudv29mCzXfM8Dkqb+XuTaQqDNzxVUqtW1YS0h0EJBCAQAAABgTrlVKavGnA+3oxoO7bU2PB01Va03WgOYPZ/nUVUSChnK9hlQ9JIrW8214obMcEjJaFj5yoCVNs2ffc/mJrNBryc579NYItKzIsXX+TyqtCQnBOp3O5hXdU4iYqo4pMHY7n0kn5VApZqvVsfWivjadulrr5eeulN609elkz4otc04ysQjynV5H53fI/+VQBmPSqC7t+yUJK1fPe77OpjfCIEAAAAAzCn3Q+dezYBiZCGQ12Do5j93mxNTqHrPlxkPsPY7iHzFan0gl3pXfPjhPn+vMbfaavAgxA2B+g1q2hUqlmJmaNbcmbEAW7VmXm9moCQ1K4Fqw6sE8lpt72UsGfXd6liu1bXK2KbYJadL2x6S3vwd6fAzZz0uGzc12aUCa2exNu33qJeUx0yge56cUCoa1v57pHxfB/MbIRAAAACAOeUGMG472KhDoJmDoaXua9M7BQr9tir1kivXlI5NVXCkY4OHQO4H+1VuJdCQQ6BcxVJ9gIHehap35Y7TatZPCOS9sSsRDavYo/0v2H28K5hmCjJIPDb5mC6PfkJG7hnpLVdKB57q+bhMPNJxa9xTO0u696mJQGvdI+GQYmZI+Wp7JdCEjlg9Nq0VErs3QiAAAAAAc8qdB7PX+GgrgVpzZzwqgbrNBcpXLCWjswOFsWR0pO1gLqftZ9BKIOecezWDtmEM354s1ZRthkDuv/erUKlPWw/vGuuz2qpTO1iq2f5Xq3du/wt2H+f3JhPr3nY13nyNeg653rpJb9v0x0obJent10j7ntTxod0qxL5zyxOyJZ1//N7d7zdDOma2AsJavaH7np4MFCRh/iMEAgAAADCniruwHSxkSPHI1McgN3joVQnkFSiMBdj4FESubCkTm9kONthr4oZIq0YwEyhIlUsn+Yo1bWC3q59qK9u2O24HczduDWs4tN9KoPHmNrmurW1P3SF99TWSbesD8b+RVh/T9ZrZREST5Zpse3oFVq3e0HduflwvOXi51i5N+vgppqTjZuvvwoPP5lS1Glq/ZjzQNTC/EQIBAAAAmFPuh87lmZjMkDHCSiAnaDDahuumY7OH4c7UKVAYS5ijqQSq1GbNBBq0fct9/p5jw6kEsm17qCFQx6AtGVHVaqgcYI5PxWrIatie13MrukpDCoH8zgRqhUCdXqMtt0pfP0OKpvVPq/9Nz8YO6HnvTNxUrW6rMmOo+S/v36qtuYouPGEfHz/BdKmo2fq7cPeWCUnSkayHX1AIgQAAAADMKbcqIxUzRzZnx7nP7DDHrT7pFgLlK3WlPSo9xhNR3xufgsjP2Oo0jJlAuSGHQMVqXVbDHmoI5B20+aig8biWJKWis98ztxKo0GUQeBCFiiUzZChmdv9oPZaISpJ35dgzG6VvvlFKLpPe+WM9pj0V9zj7TNnm78jkjCqxb938uPbMxvWyQ5b7/CmmpNtazO7eMqFs3NQ+y4JVE2F+IwQCAAAAMKfybR/aRxkCOcOCp3+49j0YukOrUsPWtEG6g7JtW7mypfTMmUADtoO57V/jiYhS0XDXn9cP9z0aZjtYp5a7oNd25zt1awcbZiVQKja9usxLx59j22bpkrOkSEp629XS2BqVqpaSkd4hkFst1r4h7PFtRf32wed0/gvWztq05kc6ZrYCsnue3Kkj14z3/NmweyEEAgAAADCn3O1gqZjpzDkZYTvYzKDBDYU6zYipN2yVat6bplof7PtYYd6J28o0sx2sUK0PtH0rV3bmISWj4Wa1x2BnHnYI5BXQSU61VdBruwFXt3awbpVfQXQKr2aKmiElo+HpFU0TW6RvnCnZdeltV0lLnPatUq2uRIBKoPb38tu3PK5wyAg8ENrlDoYu1+p64Jmc1jMUesEhBAIAAAAwp9wNS4nIqCuBPNrBelQCTQVUHpurkoOHHzO5rT3tg6H9VCv1kis7YYVhGMrEI0OrBMrGI8oOIwSqdtjA1moH8z+Au1DtPKfHHQReDDBjqOu9fIZAklOF1ZoJVHhe+sYbpPKE9JbvScsPaT2uWPUZAiWalUDN9q2q1dB3b3lCLz90RavtL6hUzFS+4gRAtbrNPKAFiBAIAAAAwJwqViylomGFQsZIQ6C8RwgUM0MyQ0bHypBurUXDqICZyZ3H0j4TyK34GCS4yVem5gwNY8ZQeyVQPBJW1Az1XcHlbvPyClPG+wjaug1rHnY7WKcKJi9jyahTCVSecFrAJp6Q3nyZtNdR0x5XrtaV8NUONr0S6Kf3PqNthaouPKG/KiDnmqbylZruftIZCk0l0MJDCAQAAABgThWqlpLND+xjI2wHK1StWcOCDcNQMhruGAJ1ay0aRQiUb4VAbZVAzX8epIUrV661foZMfPAQaLItBHL/t9/XoVxrqGF7hzb9VBkVurxnfgaBB+EVLHYynoioVJyUvnWetPV+6bxvSvu8aNbjirV6K6zqJtP6vXB+lm/d9LjWLEnolIOCD4R2paKmyrWG7nhsh5amolo9nuj7WpifCIEAAAAAzKlCpd76wD6WiGiybMm2h7txy72P1wf2dLMFxvs57tDqzlUqQTZX9eJ+oG8PMGZ+2O+HUwk0FQINczC0+7/9hkBTQdvs4CMTM2UY/YVAXhU6bptVaUjtYH5nAknSsoT0v7d9QnriJunsL0sHvdLzcSWflUCt7WClmjY/l9eND2/TBS/YW6FQ/4Oc3cDxxoe3af3qMYZCL0CEQAAAAADmVKFitSofsglT9YY9cEjR6T6e1SExs0s7WOfWopFUAjW3eLW3g7n/nB8gBGrfOOa0gw125slSTYYxFVANEgJ1e437aRF0A73ulUDDmwnkqxKoUdd7nvs7HW/dIb3+36R1Z3k+rN6wVbEavmYCJaNhhUOGcmVL377pcZkhQ286bm3QH2EaN4h7eqKsDbSCLUiEQAAAAADmVKE69UF6FMGKJFn1hipWw/MDe6ptLfbss3UOFBKRsKLh0JAHQ3u0g8XcAcADrGAvT80EysQjAwVKkvP+ZGJmq+rEqeAarBKoU5gylogEqrbqFirFIyEZhlTq8H4H5asSyLalaz+gIyd/o7+rv1X20W/t+NBys0LJTzuYYRhKx0w9n6/oitu36NXr9tTyTCzQ+WdKx6bCx/Vrxge6FuYnQiAAAAAAc6pQqbdm9YwqBOo24DntqxJo9odywzCUTUQ0UfK/uaqXnEcIlI0PYTtYW1iRjg2+cn6iVGttR5OGUwnUKUwJeu1CxVLUDCkSnv1x1zAMJSPhVrg3iG4DrdseJP30L6Q7vqlb93m3/rP2GpVrjY4PL1anNuX5kU2Y+uHdT2tnsTbQQGhX++/5kVQCLUiEQAAAAADmVHsl0DDWjXvJVzvPnUnFwh3bg3pXqZgjGQzdHiykhzATKFeuTZsJJA0WKk2Uaq3ATmoGNX3ORnKDj07VL8HbwboHM4mo2brnILoNtG657h+l339BOuGP9YfD3y9J2tklNHQrgRIeM6i8ZGIR5SqW9tsjpRMPWOb/8J2u1/zdWJGJaWW2vzXzmN8IgQAAAADMqULFas1qcYOFYW8Ic6tNkh4frlPRzoOSu7UWSdK4u/Z7SHLlmhKRsMy2KpZExJn90m8LV63eULnWUCY2uhAom3DCiEYf1UXdNrBJ/VUCdVvbnoqFVRxCO1i3gdaSpN//h/Sbv5U2vFl69d9pPBmV1H2QeNBKIPe9fPML9h7KEGf395wqoIWLEAgAAADAnCq2be0aVTtYt6Ch60wgNzzq8KF8kDYoL+1bvFyGYTTXuvc5c8etLmoNhnZe40GGQ3tVAtl2f9VKvYO24IOhvba5uRKR8FAqgbpWid1xqfSTi6VDT5fO+JwUCrVer+4hkBtW+m0HiyhqhnT2sWsCnt6b+/dj/erxoVwP84+/GjMAAAAAGAHbtpvtYKOdCVTsMhOo23awfHNeUae122OJiB58Nje0c7Zv8WrnbPTqr3rFDSumBkM3K4EGaC+bKFmzQiDn69NnBQU5X7fB0BOlmmzb9lXt0mtOTypmDqUSqGN4dd810jXvk/Z/mXTOf0th5/srx5z2qmcmSx2v6a6uj/usBPqjk/fT69av0tJUNOjxPa0eT+jDrz5E5wwpVML8QwgEAAAAYM7MnKuSjpkKh4yRVQJ5tQmlY2HV6rYqVl0xc/r3e60AH2QWjpfJcm3aenhXJu60W/UjN2POUGvGUJ/Xs21bk6Wasm3ndIdX9/O+tYZ2d5kJVG/YzSqp3gFToWppSbJzKJKMhgdqhXO1wrX2348HfyZd8U5pzfHS+ZdK5tS2rtXjCRmG9Pi2LiFQj/lIM52w/+BzgNoZhqH/72UHDvWamF9oBwMAAAAwZ1rhTPNDr2EYysaHO2xZ6r6Byg15ih7DoQvV7lUlY81ZOINs2mqXr1itQKVdJtZ/O5j7vNZg6Nhgg6bLtYaq9UZriLc0WAVXoWopHglNm4PUbjwRDXTtXoOhk9Gw53sd1KxKoId+IV32Fmnl4dKbvytFU9MeH4+EtTIT1+Pbix2vWWoNhvYXAgFBEQIBAAAAmDNuW057tY3T/jN4pUa7gsd9XO7XvKpD/FQCScMbZJ0rewcYmXjn4dW9TLWDuYOhnTP32w7mhjHT2sGSA4RAPUKbrI9ZOjOv120wdDJqqlgbXiVQKmZKD/9G+s6F0vKDpbdeJSXGPZ+z99KkntjROQQKOhgaCIoQCAAAAMCccT9It2/tGktEhr4drNtgaPdrXsOhC5V619ac8Wb4sXNI582XZw+GltQcDD3kdrA+K4smyx4h0CCVQBXLc2vbzGv7/Z0otA0a9zKsSiD3d2rp1pukb50vLT1AeuvVUnJpx+esWZrQE10qgcpUAmHECIEAAAAAzBm38qE9nMkOeeOW5AQNIUOKmbM/Arkhj9dw6F6tRcMeZJ3rMBMoPUgINGMwdCoalmH0vyLesxJogNch3yO0GQ9QZeQOGu/ZDjaE7WCFiqXjjU1acvVbpCX7SG+7Wkp1n9Gz99Kknpksq2J5378YcCYQEBQhEAAAAIA506oEamvfGUUlkFsd4rVdKt1qB/OeCeSnHWxnsTrwGesNW4VqvUM7WKTv9i33eW6FkWEYA20bcwdht4dAiUhYkXB/A72ddrDOoUfrNfZx7WK1LtvuvGlMcqrOSrW6GgPOcco8d7u+Gv1HKbtaets1Unp5z+fsvTQp25ae3OE9HNodDB03CYEwGoRAAAAAAOaM25azKyqBOlWHuIGBVyVQr5lAQapUepk5u6ddOmaqWm+02oWCyJVrMkPGtCqozCAhkEclkGEYrVXuQfUK2oK8xh3Xtrdxq2xKfbyWLVtu1Rvv/YCe1xIZb79Wyqz09bS1S5OS1HE4dKlWVzwSUig0O6wEhoEQCAAAAMCcKbRmAk2vBJoo1WTbw9m4JXUPGtJdBkPne1SpZIc4GHrmFq9p94l3PmMvzmr16VVQmXhE+Up/Z/YKgSTntejndcj3CNqCVBlNzX7qMhi6ywwoX+6+XPrGmcqb43pf5K+l7CrfT927GQI90aUSqNt8JGBQhEAAAAAA5oz7Qby9SmcsEZHVsIcyt8XVbe7M1Ir46aFAvWGrXGv4bAcbZiWQ90wgqb+17rmy1Xp++/UGrQTKzgiBxhKR1tDoIAoVS+kuwYdbZeTnNS40K8tSXa6XbG7eKgX9/aoWpWveL33vj6SVR+ifV/+rinF/FUCu5emYomao43DoYrXOZjCMFCEQAAAAgDlT6LAdTBresGX3Pp2qQ9x14oUZoYBXQDVTzAwrEQkP5ay5cud2sEws0nxM8Pvkylbr+a3rDbByfqJUUyZmKjyjZanfdrBij8HQ7rX9VBl12wLnar3fQTaEPfeA9F+vkG7/hnTyn0kX/VBPNZb0PPdMoZChtUsSenxbp3Ywi81gGClCIAAAAABzplCtKxoOKdo2r2ZUIVCn6pCY6bQbzQxFvAIqL/2GHzO5AY/3YOhmO1gf1Tv5Sm12JVDM7HvQ9GSpNqsKSOrvdXC3eaW6tG+5195Z6j18233PZv687RLN97NU8/nz3/lt6UsvlfJbpbdcKZ36cSlsNudFBQ9s9l6a1BM7OoRAVAJhxAiBAAAAAMyZQsWathlMGk0I1GvuTDJqzhoMPTVkuPuH8vFkxNfmql6mKoE6t4NN9tkOlpnxs2fiZl/Xkpz3ZeY8IKm/EKhUq6vRY5tXkGu71VvdrpeK+qwEqhakq94rXfXH0l7HSH/8O+nAU1vfzlfqSsdmvw69rF2a7DgYulitUwmEkSIEAgAAADBnCpX6rAqdUYRAxWq9a5iTjs1uj8p7bC7zMqxtZt3awbLNYGiQwdDtBh0MnU14n3GyVAu0ej3vY5uXJI0nowEHQ3erBHJ+D7rOnHr6Lqf6585vSS/5iPS2q2cNgO612r6TvZcmlStbmvCYcVSuUQmE0SIEAgAAADBnvFpq5qISKBULd6kEGs68Gj9nlDqviJf6mwmU9xoMHTNVrjVUqzcCX69bJVDDlvIBtm4VWkGbj3YwX4Oh/VQCNQeBe52z0ZD+57PSl18hVXLS266SXvb/pPDs6xV6/E510m1NfLFan7YpDxg2QiAAAAAAc8Zrdfsw165LUq3eUNVqdN1AlYqZsypDCj6qSiRp3GdA0UuuXFM4ZHhWggy8HWxG25L7M/UzF6hbCCTJs8Klk1Zo42PuUq5sqd6jysit3kp2qaZJdqoEmnxa+uZZ0s//Ujr41dKf3CDt/9KO18lVrJ6/G17WLukcApWoBMKIBf+NBQAAAIAh8RrYnImZMozhhUB+qkO82sH8zJeRhjkY2gkVDMOY9b1IOKREJBy4Haxi1VWtNzzawZohUMXSklQ00DUny94hULatgmutz2v5ad+SpgKmXLmm8WTn8zq/T2GFQrNfQ1cy5lEJdP8PpGveJ1kV6fX/Jh3zdsnjfXC5wWJ/lUAJSfIcDl1iJhBGjEogAAAAAHPGa1ZPKGQoGx9OsCL5XBvuMRjarSpJ9fhQPpaIqFSrq2IFWDnudc7y7Nk97dJxM3A7WKc5Q5nWoOlg16tYdZVrja6VQEHCOzeISfoMgXpVXPlp0XIrbYrVujP8+doPSpddKI3vLf2v30rHXtQ1AHLvI/UOr7xk4hEtSUY6VgLRDoZRohIIAAAAwJzJd1jdPqzqGmlq7szMLWTtkrHwrG1RfmcCjSenKmBWZPr/AD9Z7t5elImbgdvB8h1DoMi07/vlvidd28ECvG95nzOB2l/j7tfr3aIVDhmKmSEt2Xan9MXzpe0PSyd9UHrZRyXTX1WU3wqmTvZemtQTM0Ig27ZpB8PIEQIBAAAAmDNOJZDHpqmEObwQyEdbl2c7WMWSYahnZUb7DKMVmXjf58xXaq0tYF4ysT5CoFZY0WEmUMD2MrfKJ+sVAvkMatoFGb7t59q+hjVbFX0kcpneev/V0thqZ/PX/i/xfWbnPs0qsT5DoLVLk9r45MS0r5VrDdm2lOgxHwkYBO1gAAAAAOZMvmJ5VugMtxLIRztYzGkHs+2pwcNulZLXjJ527oyaQYdD53q0gzlr3QOGNs12r5k/e7+DpoddCRQ0BNrZMwSa3V44TXP1+zvt7+vmsdc0hz8HC4AkJ7CT1P1eXaxdmtSTO0vTBl2Xak6wlIjwMR2jw28XAAAAgDnRbWvXKEKgbhuo0jFTVsNWxZpame61vt7LsFba5yuzV7m3y/QxE6hzO1gzBAoYKnULgVLRsMIhI2A7mM/tYIO2g9Vr0nX/KH355VJxuz6a/Et9bY//I8Wzvs86/T5uG1v/7WC1uq1nJsutr7XmI1EJhBEiBAIAAAAwJ4qtWT2dQqDg68u9+PnAnvJYG17o0Ko207BCoF6VQOk+2sE6DoaOTW3bCqJbCGQYhsYSkUDDpgsVS4mIEx51M7V+vtr9elWPdrCt90tfeaX0609J686S3nuj7kmd2GoT7IffCqZOWmvit03NBSo3K4HiDIbGCBExAgAAAJgT+arbpjX7Q282EdFkqSbbtnu2Y/Uy9YG984dr98N8oWJpaXNlesHHkGFJGve5uaob27aVK9dmze5pl4lHAg9ydittMjNmDcUjIZkhI/hg6GLnEMj9epDwLl/xF7TFzLDikVCwmUDVovTbf5Ru+JwUy0rnfl1a9wZJTuhXqva/zW0Yg6ElZ038iVomaSqATDIYGiNECAQAAABgThQrndtfxhIRVesNlWsNJQasjMj7qNrwGpRcqFi+1nVnh1AJVLEaqtXtnivi81VLjYatUI/KGVe+QwBmGIZzvcDtYM7jvQZDS1I2Hmygd7Hqr+VOksYTUf/tYA/+TPrR/5F2Pi4d9RbplZ+QUstaj0tGw9oxQGg3yIp4SVo1Hlc4ZEzbEOaGUoP+vgPdEAIBAAAAmBPdqinaW6wG/VBcqFitteCdJNsqgabOV9easahU3C4Vtzl/ypPSkn2lpftLYec54ZChTGywbWZu21a2SwiUjZuybaflaWZlTyeT5ZqiZkgxc/Zr2E972USppmQ0rEjY+7XMBpzlVGgO3/ZjLBHpWm1l1RvK1rbp3M1flm7+pbTHwdJFP5T2PXnWY5NRU6U5bAeLhENaNRbX420hULFGCITRIwQCAAAAMCda7S8eH3rbQ6A9x/pfu+7eJxUNd20rc1vSwk/8j3TDN6XtD+vbO55Rdkde+sfG7CeEY9KKQ6UV66SV6/SKWFX1XETSur7O2ArEegyGltzZQf5CoHzZ6hgsZeKRvkKgTq1gkvO+bdlR8n29joOcva6d7BIwNeqq3fif+mXsE0rsaEgv/6j0og9IZszz4cloWIUB2sFyFUvRcEjRLsFiL3svTU6rBCq7lUC0g2GECIEAAAAAzIlubVrDGrbs3qdX0LBs5926JPK3OvqXG6X0ntLeJ+gX2/bV0uWr9PJjDpOSe0jJpVI0LW1/WNp6r/TsvdLmX0l3fUv/Kkl/kPTPq6RVR0l7HSWt2uD8c3ZVzzO6A5ozXWYCpVvDnP0HN7ly5589Ewu+bcxPCBSsEqiuPdJRX48dS0SmhSYtm38l/fSjSmy9V79trNfkS/9Bp59yUtdrOZVA/YdAfjfHdbN2SVK/3LS19e/dQlFgWAiBAAAAAMwJdyV2txBocggh0LRhwTM9daf067/Vvn/4qdKhrO487P/qqDf+mRRJ6C8++mO9fd999fIXHjb9OXufMOMG2/Q3X71ce5Uf0jv3m3Su+eBPJNnO99Mrpb2OlvY+UdrnJCcgCk8PUtxgx08lUL4SbAV7p6qhTNyctqLcj8lyreM8IGkqBPI70LtQsbTPsqSve48lItrY/vuwdZP087+U/vAzaXwfPf2qL+pt12T0uaX79byWUwlk9T14vFCpd32v/Nh7WVLP5ysqVetKRMMq1agEwugRAgEAAACYE+7qdq+KimFXAs0KgZ57QPrVJ6X7r5Xi4yq9+C90ys8P0IfXHKOjIglZ9YYqVsPfvJrUMj215Hj9+plD9c43vtT5WiUvPbvRCYSevlPackszGJIUSUprjncCoX1eJK05ruMq93Zu6DAZoBIo36USKB03lX8uWDvYZKmmtUs7hzZjiYjqDVuFat1Xm1eQdrBxt8qo8Lz067+VbvuaU5n1qr+RXvAePfNUUdINvq6XjIVl285A7ngfoUs+wCyjTta2bQg7eGWGwdDYJQiBAAAAAMwJdzuY14fpYYZAs1a973hM+q9TJduWXnKxdOJ7FTYzKv78x62Bv+68GL8tP7NWo8fS0t4vdP648lulx26Y+vObv5NkS5GUDl/+Yr06dKjGzBfOurbLne0TZK37ZLlzaNPvYOgjelQCuY/zE8Z0rdKaYWmsobfWr5L92T+SUS1Kx7/Lef+aW78KlZwkf8Oa3TXshYrVVwg063eqD2uXJCRJj29zQqAiM4GwCxACAQAAAJgThYolw/D+0Ou2MA0nBKprj3RzQHCjLn3/j50A6I+vl5qtQ1FJkbDRqk4Kuv1pLBHVRKnavb0ovUJa9wbnjySVdkqP/176w0+1/K7v6z+jP5H95S9Jh7xGWneWdOCpUiQx9fQ+ZgI57WCdB0MHCZQkfzOBJGmiWNPq8UTHx0lSo2GrWHOGdndl29K939dbb/8LZSJPqbL61Yq95m+k5QdPe9jUjKneIYq7Da5YrWtZj8d6yVcsLUn6m2XUyd5tlUCSVKrVFQ2HZHbYvAYMAyEQAAAAgDlRqNaVjIQVCs0OTYaxdn3qPm1VGzd8Vnr8BukN/9EKgFypmDlVCRQ4BIqoVrdVqtWV9NsmlBiXDjlNOuQ0/Vf8PbrxV9fokmOeVHjTD6SNVzqtToe8Rjr8DdKBp7ZtB/P/muTKljKdBkPHTVXrDZVrdV/VMLV6Q8Vq3V8I5ON9K9Xqsu0er/ETt0g//X/SlpvVyB6iN0/8P33itP9PBy5Pz3qo+575agdrBk/FPodD5yuW1i7xN8uok6WpqFLRcGtNfKlq0QqGkSMEAgAAABDY49uKysRNLUn1Xw3RqxUom4gMdzD003dJv/qUdPiZ0oYLZj0uFZ0KgVor2wO0g0lO+OE7BGozWbF1e/hIhc/4iPS6z0iPXi/d+31nZtE9l0vRjJKHvEavCq9RqbTW1zVt2+45GFpyflY/IZAb7HQLgbIBQqCuQdvOx6Vf/LW08QpnsPYZn9cdiVN1w9dv10Sp6n29LoPGZ3JbEN3h5EENYzuYYRha27YmvlSr0wqGkSMEAgAAABDYRV+9WScesEyfOmt939coVOtdP7AHXTfe8T6VusYilnTle6TkMun0f5U8WrbSMbMV/hTcodU+A52lzTDs2cmKVo11b4PyMi2sCZvSAS9z/rzun1uBkHH/tfpSZIcqN39RKr5eOvg0ad8XS+nlntcs1xqqN+yOW6zcipl82Zpql+vCTwgUZKtb3qtyJ/+cdOPnpd//h2SEpJd8RHrRB6RYWiuempQkbd5a0LH7LPV3vQ4SA1QC1Ru2dhZrGh+wHUxyhkM/vq3YOgvr4TFqhEAAAAAAAnsuV9HWXGWga/SqphhGCFS1GqrWG3rlk/8uPf+A9JbvScnZAYLkzJJxQ4EgVSWSdOieGUnS/U9P6qi144HPOVnuMLsnHJEOeLnz53Wf0Z/+/ed0fupWnfDgT6W7L3Mes+Jwab9TnD/7nOS0mWmqbazjdrCY217mrxrGVwiUDFIJ5A7fNqXtjzjhzx3flKyKdOR50is+Jo2tbj3+sFUZ7bdHSlfctkVvOn52NVShYikcMhQze8/UmaoECh4CPfJ8XhWroUNWZgI/d6a1S5L63R+el23bvtvygEEQAgEAAAAIxLZt5avWwK1ahYrVtXVqLBHR5ufyA9/jlNBd2vDUZdIJfyId+IqOj021bcsKMl9Gcob8ZmKm7n1qoq9z5rvM7mkJR3R/6jh9ZekpOuHCbzjtbY9cJz3yW+m2r0s3fdGpntnzSGntCbKz67S/UVUmdqTn5dzKo1zF3/voBjvZRJc19lFThuFsJeslX7F0uPGojrr5W9JjP5KMsLThfOmkD0p7HDTr8YZh6E3HrdU//GSTNj+X1wEz5gIVKs6Q6Y6DudtMVQIFbwfb+KRTkXTE6rHAz51p76UJlWp1bStUqQTCLkEIBAAAACCQYtUZ6DsZcLPUTIWqpRWZeMfvD6MSqLjzWX068p+aSB+gsVM/3vWx6ZipZybKztmaIVDS59yXUMjQYXtldW+zZSmoXLnWcXZPu0y8GVSFTWnNsc6fF/+ZUz3z5G1OIPTI9dIdl2hlrahfxaTaj/5auvsYafWx0l5HS2NrpPRKZSPR5r39vY+TPiqBQiFD2XiX961ec+b9bL1fB1/3Rf0odr3qT6alE98nvfC9UnZV1zOcfexqffpnD+i7tz6hP3/NYdO+lyv7X9vuVqD1Uwl071MTipkhHbA8Ffi5M+29zBku/fj2okq1+sBr54Fe+A0DAAAAEIg7e2XQSqBipa7ksi7tYMkBQyDbVvYXH1ZMOd30gq/qxZHus3qS0wZDO+FAkA/lR+w1pm/d/JjqDVthj41n3eTK3QMxVyYe0XNebXhmTNrnRc6fl14sNeq6/bbf69tXXaUPH5zXity9zma0xlTgs07SHbG0wj9eKd2ylzMvSZLsRvOPLdl155+NkNbnQvprs6ZVt90sZZdK8TEplnXW2Nt263lnRO7Rfs8mpHs2SYXnpe2bpe0PS9s2OwGQ7baBLdM/1s7TOe/6uPZfu3r2z+RhRSaulx+6QlfetkUfetUhirStU+81aLxdMmK2nhPUxicndeiemaGscnc3jD2xvahSta7lPmYzAYMgBAIAAAAQSCsECrCqvNN1uoUsY4mIKpb/Feaz3PktZR75sf7OukAnrjyi58PTsXDbYGhLIUOBtjWt2yurcq2hh5/L66CA82KcwdC9P56lY6Ye9tMiFwpra2J/XV5/qd75shdrxaqsVCtJW++Xcs9Iha0qbn9K1153u05J2srW89LW+yQZTkuZ+yfU/N+GpWU7t+uM8ISSN//SCXw6+KQkPSXpyuYXohlp2f5OFdIRZ0vLDpCW7q+rtizTv1/zoN6WXebjFZpy3nFr9fP7ntWvNm3Vq9ft2fp6oeo/BHLbwUoBK4Fs29a9T03o9A17BXpeJ2vaQ6BanRXxGDlCIAAAAACB5MtTa9QbDVuhgFUvLmcGSvcV8ZITNgUOgWol6Rcf18TyY/XlJ16nV/pZGx4zVajWW6vVU1HT13wZ17rVWUnSxqcmAodAuXLnVe7tMvGpDWa9uO16raAtkpBWH9P6ftiq62O//Ik+dOjBet/LZ8/gmenzP7xPl/z+MW36q9OkakEqT0iVSee1bguOLv7+RhVqtj53wTFSYomUWu65jS336MOSFHjV+ksPWa4VmZguu+WJaSFQr1CxXdQMKRI2VAgYAm3ZUdJk2dK6vbKBntdJIhrW8kxMj28vMhMIuwQhEAAAAIBA3BYa25ZyFavrjJhObNtWoWop3WM7mOS0nflplZrm9m9Ihee06dh/VuOJkK8KkVTMVL1hq2I1ArUWuQ5cnlbMDOneJyd11tH+n9doOKFTp1Xu7dJx0/csJjesy3YIl2JmWFEzpJzPUGmiVHPeE8OQYmnnj2a3ceWyVd3/zKS0/JDu52veN9UlCPRihkM659g1+uJ1m/XMRFl7jjm/G4WKpRUZ/+1UyaipUsDB0O7g73V7DT4U2rX30qSe2F5SqVpXIsJHdIzW4E2MAAAAABaV9tCg37lApZozXDrZox1M8rdufBqrKv3PZ6W1L9TjGSeN8VMh4j6mULFUrNYDV6iY4ZAO3TMTeDh0vuqGNb3PmI1HVLUaqli9K1jcgc/dfo5M20a0XlohUK8zJiK+fi+c7XDhvirJ3nTcWjVs6Yrbnmi7Xj1QcJeMhgNXAt371KTCIUOH7jn4enjX3kuTrcHQiSgf0TFa/IYBAAAACKR9mG6/c4FaVSCjCIHuvkya3CKd8qHWh3y/lUCSEyYEaS1qt271mO59akK2bft+Tm5m21YX7tygvI/gJl+pKRkNdx1gnImbvq4l+Q+B3K1uvV6DIDN8Ztp3j5ReuP9SfffWLWo0nPsEfc+S0XDgmUAbn5zQgcvT/c2o6mDtkoSemiip3rC7tkcCw0AIBAAAACCQ/LRKoP7WxBeb27dSXWag9BUCNerS7/5F2vNI6cBT20Kg3h/a3da0fMVqVqn0EQLtldVk2dKWHSXfz3FDGD8zgdyQw0/1jp9QJB1gxtBEyV/r31giolrdVqnWPWApVOpd3/9ezj9+bz2+vajfP7zNaS8M2MKXjJoqBG4Hm2zNfhqWtUuTcvOyYYZLgBdCIAAAAACBtAcQo6wEctujJooB7nHfVc5K8hf/H8kwVKhYMkOGoj7WebuhT6FqOYOh+6kEas6KcWfH+JFrvoZ+toO5QZGf4Gay3HvOUDpmtu7f83qlWmtYdzd+w7t+5i61O+2IPZWJm7rs1idUsRqyGnbgSqBigEqgrbmytuYqQ50HJDkhUPuZgFEiBAIAAAAQSGEIM4HcD9/dhgJnW2GCz2oN25au/4y0x8HSYWe0zpqK+dvy5QYS+YrVc2h1J4fumVE4ZGjjk/7nArkzlnwNhm6e0U/4lvexcSwTjwx9JpDfEKjfoM0Vj4R11tGr9eONz+ipnU7lVZDKIicE8l8J5M56GtZmMNfebSFQgkogjBghEAAAAIBA8hVL4eYwX7+bqmYqVHoPLY6EQ0pFw/7bwR78qfTsRunkP5NCoeZZ676rQ9oHQwcdMuyKR8I6cHk6YCWQ/8HQQWYC5co1ZXr8DH4HQ1v1hvIVq+OmsXbZhL8KLidoG2wGzpuOW6uq1dC3bnpckr/ZT65kzAxUCXRfMwQ6fMgh0MpsvFWplqASCCNGCAQAAAAgkHzbKu5+K4HcWSy9PrS7Q4Z7sm3p+k9L43tL68+Zuk/F8r3ly31coWL1PRhacipFNgbYEOa2Y6VjPgKWZgjjdyZQrxazjM+ZQG7YN8xKoH6DtnZHrB7TEauzuuwWZ0tYoHawSLg1m8qPe5+a0D7Lkr6CsCDCIUOrlyQkUQmE0SMEAgAAABBIvuxUhGRiZt8zgQo+ZgJJTkuYrxDokd9KW26RTvqgFJ76kB5kA5UbIEyUaqpajb4DinWrx/RcrqKtubKvx08NhvbRDhZ3B0P7awfzOxi61yYv9z0IEgL1qhJzgrbBQ4/zjlvbaqkL8p6lYmagdrCNT04OvRXM5c4FYiYQRo0QCAAAAEAg+YozcDibiPS9HazgYzuY5AQKvqqNrv9nKb1SOuots8/qMxhwB0M/l6s4ZxugEkiamiHTS65sKWT4CwDSbXOL/Fy392DoiOqN3pu8+gmBfA2GHsJK9DOOWq2Y6Xy0DfKeJQIMhp4o1fT49uLQh0K79l7qVAKxHQyjRggEAAAAIBB32HImPnglUK817GOJSO97bLlVeuQ66cT3SZH4rPv4ra6ImiFFwyE9O9kMgfqsynBnxtz7pL+5QG5Q5Wd4ddQMKWaGeraDNRq28lU/g6H9zRhqhUDJ3iGQe89uIVCjYatYrSs5YDuY5PyOvHb9KknB2sFS0bCshq2q1ej52PtGNBTatXYJlUDYNQb/GwcAAABgUclVLK1ZklTZb5WOh0K1rmg4pKjZ/f8v7Wsm0G8/LSWWSMe9c/Z9As6dScXCenay3Pzn/j4uZeMR7bMs6bsSaLJc6xnWtMvEI632p04KVUu2rd6DoePutjFLK7rkG5MBKoHCIcMJCLu8b8Vm5dEw2sEk6Y9fcoDyFUv7LEv2fnBTohlAFquWoma062PdQd+jqgR6xWErddeWna3ZQMCoUAkEAAAAIJBCs3IlG48MtB3Mz8DmniHQMxulB38snfAnUiw9+z4BN1ClYqa2NtvBBtlctW6vbKB2MD/zgFzZeO+NXnmfa+f9tpcFaQdzH9ftffM7E8qvQ/bM6MtvOy5QO5Vb6eWnJey+pya1MhvT8uZA9GE7cEVa/37hsYqZVAJhtAiBAAAAAASSLzvtYNlE92qPbgpVq2crmOSECcVqXbV6h5adGz4nRdPSCe/xvk/F/2BoyQlFBq0EkpyKkce3F30Ntc4HDIHScbPnYGi/w6bdCiTf7WBDCoFaIdWQQqB+JFohUO8gc+NTEyOrAgJ2JUIgAAAAAL41GrYK1bozGDruY15PBwWfA5vdGTSegUJhm3Tv96UNFzjtYDNUrLpqdTtwJZBbGeJ3tbwXd3bMfT6qgXKVoO1gZs/Qxq3Q6rkdLOZv29hkqaaoGfJdaeO7EmgIg6H7lWq1g3WvBCpV63poa15HjGgeELArEQIBAAAA8K1QdcOFsLKJiPIVS41G9/XiXpyhwP7awaQOIdBd35LqFem4d3if1ecGsnbt1T+DtYM5VSPuLJlu/Kxyb5eO+W8H8zsYuteMoYlSzXcVkOS/EmhY7WD9cIcwu78nnWx6ZlINWzqcSiAsAIRAAAAAAHybauOJKBs3Zdu9A4RO1/ETfGQ7bZpqNKRbvyqtfaG0cp3nc/uZO9M+qHiQgGJ5JqaV2ZivuUBBZwJl4pGeM3zcyp7e7WBuJdCuDYHc4GUu28HczWSlWvef/d4RbwYDdiVCIAAAAAC+FdoGDmeboUA/c4GKlbqvddjZTpVAj/5W2r5ZOv5dnc9aDR4Ctc8pGrRVad1eY74qgXJlq+cA53bpmNmzDS/vsx3MfW38zATKBhle3SMEcufw+KkGGxW/lUD3PjWpsUREa9jchQWAEAgAAACAb7lyWztYs0qnn7lAeZ8Dm8c6BU23/reUWCoddkbH5/ZXCeQ8NmRI8chgH5eO2Curh7bmVeoyc6Zi1VWtN1qvpR/ZuNmzDW+qHaz7zx4Jh5SIhJWvdH8P+6kEqloNlWveP/t8GAzthkDd3h/Jaelbt1dWhmHsimMBI0UIBAAAAMC3qTaeiLIJ5wP8ZCl4O1ixavmqtPGcCZR7Rtr0Q+noC6VIvONz862zBpkJFG7+rznwh/7D9xpTw3ZmynSS87nFq10mHpFtS8UOAYvkDIY2DH/VTGkfK+eDhkAdK7iahr0ivh9u1Vehy3awWr2hTc/kdMRq5gFhYSAEAgAAAOCbWzGSGrASqFCpB6oEmii23eP2S6SGJR3rPRB66h7Bgwb3scOoUDlitTNDpttcIL9tW+3SrTk+XQYvly2lo6ZCod5BViZujmQwtPs8z/M1A7qkz21jo5BsrYjvHKZtfi6vqtVgHhAWjLmLXQEAAADsdtwP75lYRG6hTNCZQFWroWq94WtrV9R02pVaYUKjLt32NWn/l0rLDuhx1uBryN0wZhgVKqvHExpLRLrOBZqqBAq2Il5qBkgdClRy5ZrvOUOZHtvG6g1bubI11BCoULGUioZ9hVSjEjNDChlT84m8bHySodBYWKgEAgAAAOBbvuxVCRSsHawYcGDzWCIyVW30h59Lk1uk497Z83mFPubOuIHRMEIgwzC0bq9s10ogt5on6Ip4qfvr7nf7muRUFuW7VBW5Z8z2EQJ1CggLPmdCjZJhGEpFza6VQPc+NaFEJKz99kjvwpMBo0MIBAAAAMC3fNt2MD9tSV4KzQ/dKZ+zeqatG7/1v6X0SumQ1/a+zwDtYH6qlPw4YvWYNj2dU63e8Px+zucA53Zu1VDXdrCK/7XzmVj3lfPuzKehVgJV/bUDjloiGlaxy3awe5+c1GGrMgrPYcUSMEyEQAAAAAB8y1fqioZDiplhhUOGMjEz8GDooOFMKwTa+bj0h59Jx7xNCvcOJArVuiJhQ1HT/8ee9sHQw7Bur6yq9YYe2pr3/L7bhhV0O5ik7sFN2VLa5zV7DYZ2g5yht4PN4Xp4Vypmdhyw3WjYuu/pSa3bi6HQWDgIgQAAAAD4lq/Upn14z7a3avlUCDirJ5uIaKJkSbd9XTIM6Zi3+75P0DBnmIOhJbUChE4tYW4blt/5Pe2P7Rbc5Ms1/5VAcbM1oNpLPyGQG1R1HgztbzvcqCUiYRU7hGmPby8qX7FaA76BhYAQCAAAAIBvhUp9WmCRiZuBB0O7a+aDVAIVi0Xp9m9IB71aGl/r63n9BA1Tg6GHU6Wy3x4pJSJhbXzSezh0ro/tYG47WLfgJle2lPF5zUzMVL5qqdGwPb/fCoGS/kMgMxxSOmZ2rQQaVtA2iFQs3HEm0MbmQG8qgbCQEAIBAAAA8C1Xnh6s9FUJ1BwMnfQ5d2csEdGx5RulwlZfA6Fb9+kjaEgNcTuYJIVDhg5bldF9HSqBchVLMTMUqGUtGQnLMHrPBAoyGNq2p96XmfqpBHIf370dbO5DoETU7Lgd7N6nJhUJGzpoJUOhsXDM/d86AAAAALuNfGV6m1E2HtFTO0uBrhF0a9dYIqJXNH6mZyMr9J6fxNT46e98Pe/R5wuBP8CnmwFXeoitSkesHtO3b35cr//c7HM/tbMUaD28JIVCRtcqm3rDVrFa933dVmVRxfJ8zo5iVVLwECibiOgX9z3r+XM/ubOkEw/YI9D1RiEVDeumh3OeZ3x8e1EHrcgoZs797CJgWAiBAAAAAPhWqNS1Rzra+vdswtSmZ/rbDpb02XL1ur3yOjB8r67MvlPLsknf91meiem161cFOls2Yep9LztQpx2xZ6DndXPusWv11M6yGvbsdqvlmZhesN/SwNc8bFVW1//hedm2LcOYvrnKbRPzO2fIDeNyZUurPDqffvvgc9p3WVKJSLAw5O0n7qOf3fes5/dWZJbr9RuCvTejcPYxa1SxvDe3Lc/EdOZRe+3iEwGjRQgEAAAAwLd8xdI+y6aCmGw80sdMoGCVQAc+cYUUMnX2uy7W2ZmVge4VlGEY+tCrDxnqNdevGdN/vf24oV7znGPX6P9ecbduf3yHjt1neoiUqzjvR5DB0JL3oOnHtxV10yPb9aFXHTwrbOrl/BfsrfNfsHeg5+xqpx6+UqcePtrfKWA+YSYQAAAAAN+clqHpM4Fylc5Dhb0UK5YMQ/4qS6yqdNd3pINPk0YcAO1OXrd+lZLRsK64bcus77lhju/B0K0QaHaYd8XtW2QY0huPWTPAaQHMF4RAAAAAAHzLzxwM3RwqnO8wXNfzGpW6UlHTX2XJgz+Ris9Lx7ytn+MuWKmYqdccsUrX3vW0SjO2W+UrQdvBpmYCtWs0bF152xadfOAe2ms8MYRTA5hrhEAAAAAAfKk3bJVq01fEZ5vDgoO0hBWrlu/NYLrjEimzSjrgFYHOuhice9wa5SuWfnLv09O+7s4E8j8Y2pz2PNeND2/TkztLOudYqoCAhYIQCAAAAIAveY9ZPtlmgDBZClIJ5HN9+eRT0kO/kI56sxRmnOlML9h3qfZemtTlt05vCZtstnUFWREvzZ4JdMVtW5SJm3r1uuENyQYwtwiBAAAAAPjiHQI1K4E85sl0UqzW/W0Gu/NSyW5IR78l2EEXiVDI0NnHrNENm7dpy45i6+vu+5T12w7WbO/LtbWDTZZr+vHGp/X6DXspHnArGID5ixAIAAAAgC8Fj1kz/bSD5SvT5wp5ajSkO74p7ftiaen+wQ+7SJx97GoZhnTlbU+2vpYLuCI+FDKUjpnTBkP/8O6nVa41dC6tYMCCQggEAAAAwBc3XEh5VgL5bwcrVq1p1/D02O+kHY9KR7818DkXkzVLknrRAct0xe1PtDa05cuWwiHD3/a1pnTMnDYT6IrbtujAFWkdtXZ82EcGMIcIgQAAAAD44lYCta8ezybcmUD+K4EKlXrvEOj2S6TYmHT4GcEPusicc+waPbG9pJse2S5pauaSr+1rTZm42Woj2/xcXrc9tkPnHLsm0DUAzH+EQAAAAAB8cUOC9gDHnQ8UZCZQoWIp1W07WGmndP810vpzpAiryXs5bd0qZWKmrrjNGRA9Wa75HgrtSsfNVqXXFbdtUThk6I1Hrx76WQHMLUIgAAAAAL54DYY2wyGlY2ag7WCFSo92sHsul6yydAytYH4komGdvmGVfnTP08pXLOXLVmvtu1+ZeES5iqV6w9b3bt+ilxy8XCuy8RGdGMBcIQQCAAAA4Is7M2ZmwJCNm74rgRoNW8VavXsl0B2XSCvXS6uO6veoi845x65RqVbXj+5+Wrl+QqDmYOjr//Ccnp2s6BwGQgMLEiEQAAAAAF+82sEkZ0OY35lApVpdtj37Gi1P3y09fZdTBcQ8Gt+O2XuJ9l+e0hW3bWnNBArCHQx9+W1bNJ6M6BWHrRjRSQHMJUIgAAAAAL4UKpZiZkiR8PSPEdl4xHclUKHqBEnJTiHFHZdI4Zi0/tyBzrrYGIahc45do5sf3a5Hni8o09za5lcmbmpnsaaf3/us3nDUasVM/5vFAOw+CIEAAAAA+JLrUGGSTfifCVSo1CVJ6ZhHyFArS3d/VzrsdCm5dKCzLkZvPHqNQkZzO1jAdrB03FS13lC13qAVDFjACIEAAAAA+FLoEC4EqgRqtpQlox4hxaYfSOWd0tEMhO7HnmNxvfig5ZJmz23qxa0cOnTPjNbtlR362QDMD4RAAAAAwAL3xPaivnjdZtm2PdB18mVLKY/wJshMoILHhrGW278hje8t7feSgc65mJ17nFPFkwk4E8h9/LnHrZXBLCZgwSIEAgAAABa4H9z9tP7+x5u0rVAd6Dqd2oyycVO5iqVGo3fIVKw67WDJmdvBdjwqPXKddNRbpBAfU/p16mEr9YpDV+iE/ZcFet4x+4zr5AP30BuPXj2ikwGYD4LFwwAAAAB2O271zc5iTXukY31fJ1+xtGc2Puvr2UREti3lq5ayPQYS5ztVAt19ufO/R13Q9/kgxSNhfeWi4wM/78AVGX3zj04YwYkAzCdE7AAAAMACl2+FQINXAnmtdneDHz8tYcVO28E2XintfaLTDgYAGAlCIAAAAGCBa68EGvQ6nu1gCedrfjaE5d3tYO2zhZ69V3rufumIswc6HwCgO0IgAAAAYIErNKtvdgxYCZQrd1gR71YC+dgQVnS3g7WviL/nCskIS4e/YaDzAQC6IwQCAAAAFji3+mbC5wYvL7V6QxWr4R0CJfy3g+WrlqJmSJFw86OIbTutYPu/REov7/t8AIDeCIEAAACABW4Y7WDdVru7lUC5cu92sGKlrlT7ZrAnb5N2PiYdcU7fZwMA+EMIBAAAACxwboAzSDuYG/B4VwI1ZwL5aAcrzBwufc8VUjgqHXZ632cDAPhDCAQAAAAscK3tYAO0g7lzhbwGQ7vBkJ/B0IWqpZQ7FLpRl+79nnTQq6T4WN9nAwD4QwgEAAAALHCFIayIzzcrgbxWxJvhkNIx02clUF0pdyj0o7+T8s+yFQwAdhFCIAAAAGCBKzQHQw8yEyjfZSaQJGXjpq/B0IVqWzvYxiulaFo6+LS+zwUA8I8QCAAAAFjAqlZD1XpD0ohDoETE/0ygqClZVem+q6VDXitFk32fCwDgHyEQAAAAsIC5rWCRsDFQO1hrO5jHTCDJ2RDmayZQpa5kLCxt/pVU3kkrGADsQoRAAAAAwALmVvCsGkuoUK2rajX6uk637WCSsyHMVyVQ1XKusfFKKT4uHfDyvs4DAAiOEAgAAABYwNytXqvHE5KknaX+qoHcMCkVDXt+Pxv31w5WrNSVDVvSph9Kh58pmdG+zgMACI4QCAAAAFjA3Dau1UucEGiiz7lAhYqlRCQsM+z9ESITN3u2g7nziY4o3CDVCrSCAcAuRggEAAAALGD55mawNc0QaEefIVC+Ynmuh3dlExHlyjU1GnbHxxSbVUmHPf8zKb2ntO/JfZ0FANAfQiAAAABgAWtVArntYH0Oh85X6sp0GAotOe1gDXuq/cz7GpYyKmrNtt9J686SQt6tZQCA0SAEAgAAABYwNwRas8RZw97vmvh8udZxKLTkDIaWpMly5xBoolTTq8O3KNyoSevP6escAID+EQIBAAAAC9hUCDTYYOhCpa5UrHPlTjYekSRNljqHTH94Nq8zQjeomtlbWn1sX+cAAPSPEAgAAABYwApVZybQimxMZsjoeyZQrmIpHYt0/H420TsEeuzxR/Wi0L0KH3m2ZBh9nQMA0D9CIAAAAGABy1csRcKGYmZY48lI/+1glZrSfiqBurSDLX30BzKNhsJHvqmvMwAABkMIBAAAACxghbatXuPJqCYGaAdLdxsM7c4E6lIJdOzOn2pL/CBp5eF9nQEAMBhCIAAAAGABy1cspaLNECgR0Y5Cv4Ohe6yIb1UCeV9/x6N363B7s55Ye2Zf9wcADI4QCAAAAFjAChWrtdVrPBnVzi6VOp1UrLqq9YYyXUIgd338ZMm7HSx/86Wy7JDCR54b+P4AgOEgBAIAAAAWsPatXs5MoODtYIWKM1y624p4MxxSKhr2rgRqNLRk8/f128aROmC//QLfHwAwHIRAAAAAwAKWb5sJtKTPwdDumvlu7WCSsyHMcybQo9crXXlWv4y+TMvSscD3BwAMByEQAAAAsIDNbAcr1eoq1+qBrpFrbvzKdBkMLTlzgTwrge6+TAUl9cyqVwS6LwBguAiBAAAAgAVs+nYwZ3jzRMC5QHnflUDm7JlA1aLs+67Wjxov0IGr9wh0XwDAcBECAQAAAAtYvr0SKBGVpMAtYW47WLeZQFKHSqBNP5RRzeuK2ot12J7ZQPcFAAwXIRAAAACwQNm2rUJ1+mBoSdoRcDh0ruKzHSzhEQLd9W0VE6t0s32IDltFCAQAc4kQCAAAAFigKlZD9YatZHR6O1i/lUA928HiM9rBcs9ID/9adyx5tSJhU/svTwW6LwBguAiBAAAAgAUqP6ONazzptoMFqwTKl322gyUiypVrajRs5wv3XC7ZDV1tn6IDV6QVCfPxAwDmEv8VBgAAABaomRU8S9xKoICDod12sFS090yghi0Vqs1qoLsuk1Yfq988P6ZDV2UC3RMAMHyEQAAAAMACNVUJ5MwESkTCioZDgWcCFSqWUtGwQiGj6+OyCSckmixb0jMbpWfvUeGQc7Q1V9HhzAMCgDlHCAQAAAAsUMVqXdJUJZBhGBpPRjQRcCZQvmz1nAckOZVAkjRZqkl3f0cKmdq49FRJ0qFsBgOAOUcIBAAAACxQeY+BzuPJSODB0PmqpXSPzWCSMxNIkiYLZenuy6WDXqV7djjPO4x2MACYc4RAAAAAwAJVqMwe6DyeiAZuB8uXLWUCVAJFHr9eyj8jbThf9z+d0/JMTMvSsUD3BAAMHyEQAAAAsEB5rXYfT0Y0EXAwdKHisx2sORNoj83fk+Jj0sGn6f6nJ3UY84AAYF4gBAIAAAAWqHzFmQmUjk4PgQJXAlWsnuvhJacSKKWSVj39C2ndWaoZET20Na/D9qQVDADmA0IgAAAAYIGaqgQKt762JBkNPBMoV/YXAmXipl4X/r0ijbJ01IV6+LmCqvUGlUAAME8QAgEAAAALVKFiKWaGZIan/s/+sWREFauhUnNzmK/r+BwMbYZDOt/8rZ6P7y2tOV6bnpmUJB3KUGgAmBcIgQAAAIAFyquNa0kyKknaWfLXEmbbtvI+K4H0/EM6xnhAN429VjIM3ff0pCJhQwcsTwc+OwBg+AiBAAAAgAXKa6DzeHONu9+WsIrVkNWwfQ2G1p2Xqq6Qrou/QpK06emcDlyRUSTMxw4AmA/4rzEAAACwQOUr9VnhzVjSCYH8DofON+cKZXq1gzXq0l3f1p3R47TFcmYAOZvBaAUDgPmCEAgAAABYoAoVS+m2odDSVDvYhM9KoNZw6WiPEGjzr6Tc0/r92GmaLNe0LV/R1lxFh+3JUGgAmC8IgQAAAIAFqlD1aAdrVQL5C4FyZScE6jkY+o5vSomlenjpizVZsrTpmZwksRkMAOYRQiAAAABggcpXrFkVPEEHQ7vtYF0HQxe3Sw/8SDryPKWSSU2Wa7r/aTaDAcB8QwgEAAAALFDOYOjp7WDxSFgxM+R7MHTBTwh0zxVSvSodfaGy8YgmSzXd9/Sklmdi2iMd6/v8AIDhIgQCAAAAFqiCx2BoyakG2hlwMHTXdrA7LpH2PFLac72yCVMNW7r9sR06dE+qgABgPiEEAgAAABYg27ZVqFqeFTzjyYjvSqCe7WDP3CM9c7d09FskSdm4M3Po0W1FHc48IACYVwiBAAAAgAWoWK3LtuVZCTSWCBAClXuEQHdcKoWj0vpzJUnZRKT1LeYBAcD8QggEAAAALECFanO1e6d2MJ+DoQsVS4YhJaPh2d+0qtI935UOea2UXCppqhJIYjMYAMw3hEAAAADAAlSo1CVJ6djs8GY8GfG/Ir5iKR01ZRjG7G8++BOpuK3VCiZJ2YQTOkXChvbfI93HyQEAo0IIBAAAACxA7lavmSviJWk8GdVEsSbbtnteJ1+2Og+FvvNSKbNKOuDlrS+5lUAHrsgoavJxAwDmE/6rDAAAAOxGfrLxaVWses/HdRvoPJ6MqFpvqFjtfZ1C1fJsKVPuGekPP5M2nC+FpqqN3JlAh7EZDADmHUIgAAAAYDfx8HN5/fE3b9dPNj7T87GtSiDPmUBOULOz1LslLFf23jCmu74j2Q3pqLdM+3I2bmrfZUmdcvDyntcGAOxaHeo6AQAAAMw3E83Q5tnJcs/H5ruEQGOJqCRpZ7Gq1eOJrtcpVDxCINt2WsHWvlDa48Bp3zLDIf3mwy/reT4AwK5HJRAAAACwm3Dbt7ble2/2mhoM7d0OJsnXmvi8Vwi05Vbp+Qeloy/s+XwAwPxBCAQAAADsJtwWr+d9hUBuJdDs7WBLkm4lkI8QqOwxE+jOb0qRpLTurJ7PBwDMH4RAAAAAwG6iUHWCnW2FSs/H5rtuB3MqgXYUe4dJ+YqlTPt2sGpRuudK6fAzpRjDnwFgd0IIBAAAAOwm3BYvf+1glpLRsEIhY9b3xpobvCZ6DIa2bXt2O9j910rVnHQUrWAAsLshBAIAAAB2E0W3EijfuxKoULWU9KgCkqR4JKxEJKwdhe5hUrnWUMOeMVz6zm9KS/aV9jnJ97kBAPMDIRAAAACwm3ArgZ4vVGXbdtfH5it1pT3mAbmWJCM9V8TnKs7302472I7HpEd+61QBhfgoAQC7G/7LDQAAAOwm3GHPVavRmvnT7bFe6+FdY8loz8HQUxvGmmHSXd+WZEgbLvB/aADAvEEIBAAAAOwmCs0V8VLvuUD5HiHQeCKinT0GQ+fLTtCUjkWkRkO681Jp/5dI42sDnBoAMF8QAgEAAAC7CXcmkNR7Q1hh5kDnGZakArSDxUzp0eulnY9LR70lwIkBAPMJIRAAAACwmyhU6go3t30936MSqGc7WCLasxJoqh3MdKqAYmPSYacHPDUAYL4gBAIAAAB2E4WKpb3G45L8tIP5GAxdrHUdMJ1vVgJljKJ03zXS+rOlSKKPkwMA5gNCIAAAAGA3UaxaWrskKan3mvhi1VKqw4p4SRpPRmQ17K4DpvPNSqClj/5Askq0ggHAbo4QCAAAANhNFKp1LUlGlY2b2lboXAnUaNgqVuvdB0Mno5LUdUOYOxg6dd9l0vJDpdXH9HlyAMB8QAgEAAAA7CaKFUvJaFh7pGN6rkslUKHqbvXqvh1Mkia6DIcuVCwdFHpS4SdvkY66UDKMPk8OAJgPCIEAAACA3YS79n1ZOtq1Hcwd6NytEmhJyqkE2tFlOHS+YumC6PWSEZY2nN/nqQEA8wUhEAAA+P/Zu+8wxw763OPfoz4qU3Y0u+v1ept7r9gYFzAYDKaZ3ntLCCGXkgBJIEAgIdzATSEQCBAwGEwHUwy2aTYu4N7b7nqLvW2aepfO/ePoaDQalSONpuzO+3keP16PNEdnRhp5zzu/IiIHAdO0W7zcjIb8bQdD23N+Qm0GQ9uVQO3awdLZHC/gBjjmEgiv7vHMRURkuVAIJCIiIiJyECiUK5QqJkFftRKozUygdL5zO9hQ0A6BWh9n4/QtRJm2WsFEROSgpxBIREREROQgkLFbvHxuRsN+pjMFSuVK0/uma5VA7WYCdR4Mfdb0z4kbQ1YlkIiIHPQUAomIiIiIHARSdcFONOzDNGG6RYCTclAJ5PO4CPncLY8xsWc7Z+Vu5pHDng9u7zzPXkRElgOFQCIiIiIiB4FMYWbY82jID8BkuvlwaHs7WLtKILDWxMeyzdvBHr/2C7gwiV70zl5PWURElhmFQCIiIiIiBwE72An63IyGrVauVsOhU3WtY+0MB73Em1UClQps3Pk9bvWdxeajT5zHWYuIyHKiEEhERERE5CCQqVv7Hq2GQBMt1sQ7mQkEMBL0NV0RP3Hb9xmpTDNx3Ovnc8oiIrLMKAQSERERETkI1GYC+erawVpUAqXzJQzDqhpqZyjoJZadWwlUuOmL7Kis4dSLXjzPsxYRkeVEIZCIiIiIyEEgU5vz42ZowIvbZbScCZTKlwj5PBiG0faYwwPeudvB9t3HusRd/G7w+axfFe7LuYuIyPKgEEhERERE5CCQrg6GDvo8uFwGq0K+tpVAIX/7KiCw2sFimQKViln7WOz6z5MzvfifpFYwEZFDjUIgEREREZGDQCY/UwkEEA37mWgZApU7zgMCazB0xYRk9dhkY4Qe+gFXVc7j4jOO68+Ji4jIsqEQSERERETkIGDP+Rnw2iGQr207WNhRCGQNmLY3hJl3fQtvJcc9617GWMTfpzMXEZHlQiGQiIiIiMhBIF0oz5rzMxrytd0OFvI5CIEGvADEsgWoVMjf/CVurxzNKU96av9OXERElg2FQCIiIiIiB4FMoTRr29do2N9yJlAqX3LUDjYSskKg6UwRtv+WQOIxvlW5hEtOXNufkxYRkWVFIZCIiIiIyEGgcc7PaNhHplCubQ2rlymUCTsYDD00YLWDxTIFzD/9D1MMkjvmuQxVK4REROTQohBIREREROQg0LjxKxqyZvY0qwZKO6wEGg5aYU9xcic8+iu+VbqIS0/b3KczFhGR5UYhkIiIiIjIQSBdKBH0za4EAphMzw2BHA+Grlb8bHzsO5gm/Nj1LJ5x/Oo+nbGIiCw3CoFERERERA4CmUKZUMNMIIDJhuHQpXKFfKniqBLI43Yx6q9wwt4f81vO5OQTTyLg7dxGJiIiByeFQCIiIiIiB4F0vkSwfiZQqFoJ1NAOls6XARyFQAAv8t9KqBzjq4WLecGp6/p0tiIishwpBBIREREROQik82XCTdrBJtKzK4FS1UHRTgZDA7y4ci3bK2t5wH865x8d7dPZiojIcqQQSERERETkIJAulAjWBTtBn4egz92kEsgKgRxVAo0/zAmlB7iyfBHPOWUdXrcuD0REDmV6lxcRERERWeZM06zOBJod7IyGfXNmAqXsEMjnIAS643LKuPlh+UK1gomIrADOGoVFRERERGTJ5EsVyhVzViUQQDTsn7MdzHElUCkPd3+bx6JPY1XxcM7etKqv5ywiIsuPQiARERERkWXODnYa176Phvw8Ecs2vW+o00ygh38BmUmOetE7+eWRF+JyGf07YRERWZYctYMZhvFswzAeNgxjq2EYH2xy+xsNwxg3DOOu6j9v7f+pioiIiIisTJmCtfEr2NDiFW3aDmbdtzEwmuOOy2HoCDjyIgVAIiIrRMdKIMMw3MB/Ac8EHgduNQzjKtM0H2i463dM03zXApyjiIiIiMiKli7Yc35mV/eMhn1MpQtUKmYtyHHUDja9E7b9Fp72QXA52yImIiIHPyeVQGcDW03T3G6aZgG4Enjhwp6WiIiIiIjY0tXqnmCTdrBSxSSRK9Y+lmrROjbLnd+0/n3aa/p7oiIisqw5CYEOB3bX/ffj1Y81eolhGPcYhvF9wzCO6MvZiYiIiIhI3UyguZVAABN1LWHpfAm3y8DvafFX/UrZCoGOegYM66/tIiIrSb9WxP8U2GSa5inAtcDXm93JMIy3G4Zxm2EYt42Pj/fpoUVEREREDm2ZajvY3JlAfgAmUjMbwtL5EiGfG8NoMedn63WQ3ANnvH5hTlZERJYtJyHQE0D9rwjWVz9WY5rmpGma9q8fvgyc2exApml+yTTNs0zTPGtsbKyX8xURERERWXHsdrBQQwhkVwJN1oVAqXy5fSvYHZdDaAyOeU7/T1RERJY1JyHQrcDRhmFsNgzDB7wSuKr+DoZhHFb3ny8AHuzfKYqIiIiIrGy1SqDGdrCQVQk0mZ7dDtZyKHRyPzx8NZz6KvD4FuZkRURk2eq4Hcw0zZJhGO8CfgW4ga+apnm/YRgfB24zTfMq4N2GYbwAKAFTwBsX8JxFRERERFaUVmvfR4JeDKOhHazQJgS66wowy2oFExFZoTqGQACmaf4C+EXDxz5S9+cPAR/q76mJiIiIiAhYlUAugznDnj1uFyNBH5MNg6GbtoOZptUKtvE8iB690KcsIiLLUL8GQ4uIiIiIyAJJ58uEfJ6mw55HQ75ZM4HS+TKhhrYxAHb8AaYfUxWQiMgKphBIRERERGSZyxRKc+YB2aJh/6yZQKlWM4HuuBz8Q3D8CxbqNEVEZJlTCCQiIiIissy1DHawNoRNNswEmtMOlpmCB34Cp7wMfMGFPFUREVnGFAKJiIiIiCxzmUJ5znp4WzTsZyLVYTvYvd+Dcl6tYCIiK5yjwdAiIiIiIrJ00vkSQV/zdrDRkI9ErkShVMHEpFg2CTXe964rYO0pcNipi3C2IiKyXKkSSERERERkmWu39n007AdgKl0gXV0lP+u++++HvXfDaa9Z8PMUEZHlTSGQiIiIiMgyl8mX284EAphI5UnnS0BDCHTXt8DlhZNftuDnKSIiy5vawURERERElrl0oTS3xasqWg2BJtMF3C5rhXxtMHS5CPd8F465BEKji3KuIiKyfKkSSERERERkHiZSee7fE1/Qx8jkywRbDIYeDVntYBPJJpVAW38N6QNqBRMREUAhkIiIiIjIvHz+t9t47Zf/uGDHN02zuva9xWDoWiVQnlQ1BKrd964rIBiFo5+5YOcnIiIHD4VAIiIiIiLzMJ7KM50pkswVF+T4uWKFignBFjOBwn4PPo+LyVTDYOjMFDx8NZzyCnB7F+TcRETk4KIQSERERERkHuJZK/zZF88tyPHThWqLV4uZQIZhEA35mEgVZtrBfB649/tQKcJpr16Q8xIRkYOPQiARERERkXlIVEOgvQsUAmWq1T2tZgKBtSZ+djuYx2oFW3sKrD1pQc5LREQOPgqBRERERETmIbHAlUCpZmvfG4yGfdV2sGoIFH8Y9t6lgdAiIjKLQiARERERkXlI5Ba4EshuB2sxGBogGvYzmcqTKpTwuV14770SXF44+WULck4iInJwUggkIiIiItIj0zRnZgIlsgvyGOmCk3YwHxNpqxJoyGfCPd+FYy6B0OiCnJOIiBycFAKJiIiIiPQoV6xQLJvAQs4EclAJFPJTKFXYn8hzkfdeSB9QK5iIiMyhEEhEREREpEd2FRAswkygDpVAALsmMzzf/B0Eo3D0MxfkfERE5OClEEhEREREpEf2PKBVId8CzgSy2sHaD4b2AxCf2seTi3+CU14Bbu+CnI+IiBy8FAKJiIiIiPTIrgQ6dk2EeLZYG+LcT+nqMYO+1u1goyGrEuhZlT/gpQSnvbrv5yEiIgc/hUAiIiIiIj2y18MfuzYCLMxcoEy+jNtl4Pe0/qt7tFoJ9FL39ezyHwVrT+r7eYiIyMFPIZCIiIiISI/iDSHQQswFSuVLhHxuDMNoeZ9VIR/HGrs4xfUYd4xc2vdzEBGRQ4NCIBERERGRHi1KJVCh1HYeEIDP4+IN/t+RN708svrZfT8HERE5NCgEEhERERHpUTxrzes5Zo1dCZTt+2OkC+W284AAKKR5Adfz88o5GKHRvp+DiIgcGhQCiYiIiIj0KJErEvS5Cfs9C7YhLJPvXAnEvd8nTIYrSs/ofF8REVmxFAKJiIiIiPQoni0yNGCtYl87GFiQmUDpfJmQr02wY5pw21d43LeF281jCCsEEhGRFhQCiYiIiIj0KJEtMhiwQqDDhgILUgmULpQI+du0g+25A/bezR2rLwOM9oGRiIisaAqBRERERER6NKsSaCjAvsRCDIYuE2wX7Nz6VfCG2H348wHUDiYiIi0pBBIRERER6VEiV2JwwApdDhsKMJUukCuW+/oY6XybSqDsNNz3AzjlZQwOrwJQO5iIiLSkEEhEREREpEeJbJHBWiXQAEDf5wKl86XWLV53XwmlLJz1FqJhPwCRgEIgERFpTiGQiIiIiEiPGmcCAX2dC1SpmGSKZYLNqntME277Kqx/Ehx2Chcdt5pPXHYSJx8+1LfHFxGRQ4tCIBERERGRHpQrJsl8adZMIIB9iWzfHiNXKmOaEPI1aQfbcQNMPAJnvRmAgNfNa5+8EZfL6Nvji4jIoUUhkIiIiIhID5K5IkCtHWwhKoHSeWu+UNNKoNu+CoFhOPFFfXs8ERE5tCkEEhERERHpQTxrhUB2JVDQ52FowNvXmUDpfAmAcONg6OR+ePCncNprwDvQt8cTEZFDm0IgEREREZEeJLJWQDNYN4j5sKFAfyuBCtZjzFkRf+flUCnVWsFEREScUAgkIiIiItKDxkogsOYC9bMSKFOw2sFmbQerlOH2r8Pmp0L0qL49loiIHPoUAomIiIiI9CDRMBMIFqASqNoOFqxvB3v0WojvVhWQiIh0TSGQiIiIiEgPmlYCDQ4wkcpTKFX68hj2YOhw/WDo274K4TVw3HP78hgiIrJyKAQSEREREelBokkIZG8I25/oTzXQzEygaiXQ9E549Bo44/Xg9rb5TBERkbkUAomIiIiI9CCeLeJ2GTMBDdZMIOjfmvhMtR2sNhPotq+A4YIz39iX44uIyMqiEEhEREREpAeJXJGhAS+GYdQ+dlgtBMr25THS1cHQQb8bilm443KrDWxofV+OLyIiK4tCIBERERGRHsSzpVnr4WGmEqhfG8LS+RJet4Hf44Z7vw/ZaTjnHX05toiIrDwKgUREREREepDIFmfNAwKIBLyE/Z7+tYMVygR9HjBN+NMXYfUJsPG8vhxbRERWHoVAIiIiIiI9iGeLs9bD29YOBfpaCRTyuWH3H2HfvXD226Gu/UxERKQbCoFERERERHqQyDUPgQ4bCrC3T9vBMoUyQb8H/vhFCAzBKS/vy3FFRGRlUggkIiIiItKDRLbIYKB5CLSvT4OhU/kSR3ji8OBVcPrrwBfqy3FFRGRlUggkIiIiItIl0zSJN5kJBLB2aIADyTzFcmXej5MplHhe8VdQKcNZb5738UREZGVTCCQiIiIi0qVcsUKxbDI44Jlz22FDAUwTxpP5eT9OPpfjGZlfwNHPhNEj5308ERFZ2RQCiYiIiIh0KZ4tArSoBLLWxO/tQ0vYWdkbGC5PwdlaCy8iIvOnEEhEREREVpRUvjTvYyRyVgjUaiYQ0Jc18S/M/5wJ33o48unzPpaIiIhCIBERERFZMSZSec74x2v5/SPj8zpOu0qgwwYHAOa/Jn7PnZzKw9yx5qXg0l/bRURk/vR/ExERERFZMfbGchRKFR7cm5jXcRLVEKjZivjBAQ8DXve8K4HMP/0PadPPo+teOK/jiIiI2BQCiYiIiMiKkay2cc23SqddJZBhGNU18fN4jPQk3Pt9fli+AG9oqPfjiIiI1FEIJCIiIiIrhj3L50ByfiFQrRIoMHc7GFjDoec1GPrOyzHKeS4vP4uQv/ljiIiIdEshkIiIiIisGImsNRR6/pVA1nGatYOBFQL1/BjlItz6FbLrz+dRcz0hn0IgERHpD4VAIiIiIrJi2JVA+xP5eR8n6HPjdTf/6/S6oQH2J/OUK2b3B7/zGxDfzf4T3wpA0Oeez6mKiIjUKAQSERERkRXDbuPan8hR6SWgqYpni03nAdnWDgUoV0wmUl2GTcUs/P7TcMSTObDmQgC1g4mISN8oBBIRERGRFSORs9q4ShWTyXSh9+NkiwwGWodAhw0FALrfEPan/4HkXnjGR0gXyoBCIBER6R+FQCIiIiKyYtjtYGBVA/XKSSUQwN5YF8Ohcwn4w2fhqIth03mkC1ZgFVI7mIiI9IlCIBERERFZMRLZEi7D+vN8hkMnciUGB1pX6Bw2NAB0WQl08+cgOw1P/zAAmbxVCRRUJZCIiPSJQiARERERWTESuSIbR0MA7JtHJVAiW2y5GQxgJOjF53E5f4z0BNz8X3DCZbDuNOtDqgQSEZE+UwgkIiIiIitGIltkSzSEy5hfO1inmUCGYXDYUMB5JdANn4ViBi76u9qH0nkrBApqRbyIiPSJQiARERERWTGSuRLDQR9jEX/P7WDlikkyX2o7Ewhg7WCAfXEHM4Hij8OtX4bTXg1jx9Q+nC6U8bld+Dz6K7uIiPSH/o8iIiIiIiuG1cblsQKaHiuBktXh0u3awQDnlUC//xfAhKd+cNaHM/kSQb9awUREpH8UAomIiIjIilCpmKQKJQYDXtYMBnpuB4tnrRCoYyXQ0AD7EzkqFbP1nSa2wp1XwFlvgeEjZt2ULpQJqRVMRET6SCGQiIiIiKwIyXwJ07QqeNYOBXpuB0tkrVk9g4H2Ac1hQwGKZZPJdKH1nX77SfAE4IL3zbkpnS8RUiWQiIj0kUIgEREREVkREtUKnsGAhzWDARK5EtlCuevjOK0EOmwoALRZRb/3Hrj/h3DuOyE8NufmdKGsodAiItJXCoFEREREZEVIVGf5RAJe1g5WA5oeWsISjmcCDQCwt9Vw6F9/HALDcO67mt6cUSWQiIj0mUIgEREREVkRam1cAx7WdqrSacP5TCDrMZoOh37kV7D1Wrjw/TAw3PTzNRNIRET6Tf9XEREREZEVobbVK+Al4LUqbHoZDl1rK+sQAo2GfHjdxtwQqJiDqz8A0WPg7He0/HxrJpD+ui4iIv2j/6uIiIiIyIqQyFmVQEMDXkZCPqC3drB4tojbZRDytW/VcrkM1gwG2NfYDnbz52D6MXjdj8Dja/n5mUKJYIfHEBER6YbawUREZNGVyhVe9aVbuGnrxFKfioisIDODob2E/R7Cfk9P7WCJXJHBgAfDMDre97ChAE/E6kKg2G64/l/h+BfAkU9v+7npfFmVQCIi0lcKgUREZNFNZ4rcvH2S23dOL/WpiMgKYg90DldXu68Z9PfUDhbPljrOA7KddsQwd+2O1R6ba/7e+vcln2z7eeWKSbaomUAiItJfCoFERGTRpfNWS0aqUFriMxGRlSSRLRH2e3C7rAqetUOB3raDZYsd5wHZLjlxLcWyyW8fOgDbfwcP/BgueC8Mb2j7eZnq+6O2g4mISD8pBBIRkUWXrl7cZPLlJT4TEVlJ7DYu25rBAPt73A7mtBLojA0jRMN+rrvvCWsY9MgmeMq7O35epmC9PwZVCSQiIn2kEEhERBZduhr+2BVBIiKLIZmbXcGzdjDAgWSeSsXs6jhWmOQsBHK5DJ55whrWP/INGH8Inv0p8AY6fp79/qhKIBER6SeFQCIisuhq7WAKgURkESWypVnhzdqhAKWKyUQ63+VxnLeDATx/i4t3Gt9j4rCnwjHPdvQ5diWQZgKJiEg/KQQSEZFFZ4c/ac0EEpFFlMgVGRyY3Q4GsD/uPAQyTdMKkwachzPnbPsP/BT5avjt4GCjGMy8TwZVCSQiIn2kEEhERBadPfA0rZlAIrKIErkikcDsdjCgq+HQuWKFQrnieCYQu27Bfe+V/G70FVy53U+pXHH0abXB0KoEEhGRPlIIJCIiiy6lmUAisgSsdrCZUGXtUPchkL3q3dFMoEoZfvF+GDycyvnvYypd4Lad044exw7JNRNIRET6SSGQiIgsOjv8UQgkIovFNM05g6GjYT9ul9HVhrB41gqBHFUC3f6/sO9eeNYnuODEjfg8Ln51/z5HjzOzIl6VQCIi0j8KgUREZNHZs4DSBbWDicjiSBfKVMzZFTxul8FY2N9dJVA1BOo4GDozBb/5BGy6AE58ESG/hwuOinLN/fsxzc7byOyKSa2IFxGRflIIJCIii66+EsjJxZCIyHzNhDezQ5U1QwH2dxECOa4E+vXHIZeAS/9vbRj0JSeu5YlYlvv3JDo+TsYeDO1TO5iIiPSPQiAREVl09qyLUsUkX3I2JFVEZD7sWT6Rhlk+awf97OuiHWxmJlCbCp09d8LtX4Nz3gGrj699+BnHr8ZlwDUOWsLShTI+jwuvW39dFxGR/tH/VUREZNGl6mYBaS6QiCyGRNZ6r2kc6Lx2MNBVO1g806ESqFKBX/w1hKLwtA/Oumk07OdJm1bxq/v3d3ycTKFEWPOARESkzxQCiYjIorMHnlp/1lwgEVl47drBkrnSrPeltsfJVcOkViHQ3d+Gx2+FZ34cAkNzbr7kxLU8vD/Jjol028dJ5UtqBRMRkb5TCCQiIovOHnhq/VmVQCKy8JL55qvd1w5W18Q7bAmLZ4sEfe7mbVq5OFz3D7D+SXDKK5t+/rNOXAPQcUtYJl8mpKHQIiLSZwqBRERk0aXzM20OagcTkcVQawdrqOBZY4dADlvCEtli61aw330K0hPWMGhX879mrx8JctLhgx1DoHShRNCvSiAREekvhUAiIrLo0vkSqwf9gCqBRGRx2O1gkYaBznYI5HRDWDxbnFNNZB3gAfjjF+HMN8K609se45IT1nLHrhgH2jxmfVguIiLSLwqBRERk0aXzJdZErAsvzQQSkcWQyBUZ8M5t41o7ZLeD5R0fZ04lkGnC1X8DgUF4xkc6HuOSk9YCcM0DrQdEZwplzQQSEZG+UwgkIiKLyjRN0oWyKoFEZFElsqU5Q6EBwn4PYb+ni0qgJse5/4ew4wZ4+ochuKrjMY5eHWZzNNS2JSxdKGkmkIiI9J1CIBERWVT5UoVyxWR1xAqBNBNIRBZDIteijQtYM+h3PBg6kS3OnitUzMG1H4U1J1utYA4YhsGzTljDzdsmiVfb1Bpl8mXNBBIRkb7TrxdERJZYPGNdAAwFWwwaXQbS+RKZQpmxanAz32PBzBwOtYOJyGJI5kot17qvHQp0NRh6Vpj0py9CfBe88Cfgch7aPOvEtXzx+u1cftMOnrR5bvVQMl8ipJlAIiLSZ/o/i4jIEnvf9+6mXKnwv286e6lPpaVPXf0Qf3xskmve89R5HytdXQ8/HPThdRtqBxORRZHIFRkN+ZretmYwwM3bJjseo1wxSeZLMzOBMlNw/Wfg6GfBlqd1dT6nHzHMuqEAn7n2kZb3iYbmH7yLiIjUUwgkIrLEnohlyRaWdxBy3544e2LOfkveiR36hHxugj6P2sFEZFEkskU2jYaa3rZ2MMCBZJ5yxcTtMloeI5mzKjdrFUW//zQUkvDMj3d9Pi6Xwff+/CnsnEw3vd1tGJy2Ybjr44qIiLSjEEhEZIklc0UmUwVM08QwWl98LBXTNNl2IEUqX+p4geREuhp4harDWFUJJCKLIZFrPhgarHawcsVkMp1ndXVzYdNjZK33q6EBL0xug1v/B05/Haw+vqdzOnx4gMOHB3r6XBERkV5oMLSIyBJL5kpki2WSyzQMmUwXSOSsc+tHYGNX/oT8HkJ+N5m8ZgKJyMIyTXPuLJ869oyy/R3WxNtDnAcDHrjuo+D2w0V/29dzFRERWUgKgURElpBpmrVg5YDDoaSLbfv4TKuC3QoxH/ZMoLDfQ8jvqVUGiYgslFyxQqlith4MXQ2BOg2HTlTfAw9P3gMPXgXnvRsia/t7siIiIgtIIZCIyBLKFMqUKyYABxLtfwO9VLaPp2p/Tub6VwkU9LkJ+dQOJiILzw5vWlUCrR1yFgJZlUAmm+/4Zwivhaf8ZV/PU0REZKEpBBIRWUL1ocr+5DKtBJqorwTqQwhUrfwJqx1MRBZJotrGFQk0nwkUDftxuwz2xztUAmWLXOr6I8EDd8DT/w58zQdNi4iILFcKgUREllB9e9X+ZVwJZM+C7k87WP1MIFUCicjCSzRu9WrgdhmMhf0dK4GS6Qwf8FxJeex4OO01fT9PERGRhaYQSERkCSXqK4GW8UygY9ZEgP5UAqXyZbxuA5/HRcinmUAisvDsrV6DLSqBANYMBTq+Dx+589tsdB3A9ax/BJe7r+coIiKyGBQCiYgsofrKmuU4E6hQqrBzKsNpRwwD/asECvmtC7GQ31OrDBIRWSidKoEA1g762deuHSw7zZN3f4WbOQXjqIv7fYoiIiKLQiGQiMgSsiuBomEfB5bhTKBdUxnKFZNTqyFQok8zgUI+KwQK+90UyyaFUmXexxURacV+72o1GBqsDWFt28Fu+AyBcoovB94EhtHvUxQREVkUCoFERJaQXVlz5Fh4Wc4EsjeDHX/YID63q/bb9PlI50uE6yqB7I+JiCyUToOhwWoHS+ZKZJq1qE7vgD9+kRtDz2Q8fMwCnaWIiMjCUwgkIrKE7Bk7R60Osz+RwzTNJT6j2ezNYFvGQgwOePq0Ir5M0G/N0rArgjQcWkQWUiJXxOdxEfC2nuOzdrC6Jr5ZS9ivPw6Gm68HXstQm5YyERGR5U4hkIjIEkrmirhdBpujIfKlSm146XKxfTxFNOxnMOAlEvD2aTB0k0ogDYcWkQWUyJbatoJBXQjU2BL2+O1w3w/gKe9ie2Go43FERESWM4VAIiJLKJmzApHV1YuP/ctsLtD28TRbxkKA1UbRj8HQmbqZQKFqRVA6X573cUVEWknkigwOtG4FA6sdDBo2NZomXPP3EBqD8/7KCpNUCSQiIgcxhUAiIksomSsRCXhYE/EDy29D2LbxFEfOCoH63A6mmUAisgiSuS4qgeJ178MP/Rx23QRP+xCmL0wi2zlMEhERWc4UAomILKFkrkgk4GXNYJPfQC+x6XSB6UyRI8fCAET83r5UAs1qB/MpBBKRhWeFN+1DoJDfQ8TvmXkfLhfhun+A6DFwxhvIFSsUyhXNBBIRkYOaQiARkSWUqFYCrR60KoGWUzvY9glrM9iWPlcCZQqlWgVQuDYTSO1gIrJwErli281gtjVDgZnB0Ld/DSa3wjP/Edye2nZEzQQSEZGDmUIgEZElZLUoeAj6PEQCnmXVDrZtvLoZLFqtBOrDYOh8qUyxbNYNhrZnAqkSSEQWjpPB0GC1hO1L5CAXh9/9M2y6AI65BIB4dc28KoFERORgphBIRGQJJXPF2oXJ6oifA8upEmg8jddtsH5kALAqgVL5EuVK72vs7QHQQd/smUBaES8iC8nJYGiANYMBqx3sD/8GmUl41j+CYVjHqIZAGgwtIiIHM4VAIiJLyB4MDfbFx/KpBNo+nmLjaAiP2/pfhX2e8wls7IofO/zxe1y4XYYqgURkweSKZQqlirNKoCE/7uQTmLd8Hk55Baw7vXabKoFERORQoPUGIiJLxDTN2mBosEKgW3dM9eXY5YqJ22XM6xjWZrBw7b/tC6hEttjzRVC6YIU9djuYYRiEfG4ymgkkIi2YpolpgqvH9zS7jdVJBc/awQDvcX8H0zSZPOevqdRVZz4Ry1rHcTBbSEREZLnS/8VERJZIulCmYs5U2Kwe9HMgkcc0TQyj9wBnXzzHU//vb/nam87m3CNHezpGqVxh11SGZ524tvYxu5ViPnOBGiuBwAqE1A4mIq387Y/uYzyZ48tveFJPnz8z0LnzX3uPNbdzlutGvph/Hv/yn48Aj8y5z6qQr6fzEBERWQ4UAomILBF73XqtEigSoFCuEMsUGZnHRcZjE2nypQo/ueuJnkOg3dNZimWTLdFQ7WP2ec5nTXyqOhMoVJ0JBBD0e9QOJiItPbwvwdYDqZ4D8tosn07tYKbJWQ9/lqJviFUXfpBPeCNz7nLYUIDhoEIgERE5eCkEEhFZInZFTX0lEMCBZH5eIVA8WwDg1w8doFIxe2qh2D5ur4efaQezz3M+lUCZJpVAIb9HK+JFpKVYtkgiV2I6U+ypCidRawfr8NfeR6/BtfMG/M/5NK8456ReTlVERGTZ02BoEZElMlMJNDMYGrA208zDdMY67ngyzz1PxHs6xvbqevgjx5pUAuXnUwk0eyaQ9We3KoFEpKVY9T3NDqe75agSqFyCaz4Mq46EM9/U0+OIiIgcDBQCiYgskUStEmimHQz6EQJZlUAuA3794P6ejrF9IsWqkG9W20M/KoHssCdY3w7mUzuYiDRXqZjEqu9p2yfSPR3D0WDoO78BEw/DMz8GHrV7iYjIoUshkIjIEqldmDRpB5uPeKaIz+PirE2ruPaB3kKgbePpWVVA0KcQqNr2pcHQIuJEMl+iYlp/fqzHEGhmMHSLECifhN/+E2w4F457Xk+PISIicrBQCCQiskQaB0MHvG4GA56+VAKNBL088/g1PLQvyePTma6PsX08xZZoeNbH/B43Po+rdkHVi3S+hMdl4PfM/O8n5NeKeBFpzq4CAnhsvMcQKFvE4zIIeFv8tffG/4D0AXjWJ2AemxlFREQOBgqBRESWSONgaLDmAh1IzK8SKJYpMjzg4xnHrwbgNw8d6Orz49kiE6kCWxoqgcCqWppvO1jI75m14SekSiARacGeB+TzuNg+0eNMoFyRwQFv881iiT1w03/CiS+G9WfN51RFREQOCtoOJiKyRJK5Im6XMWs+zprBAPuT86sEimWKDAe9bBkLs2UsxLUP7Of1527q/In5FOy6hYkDCS50PcRZ5Txs3w2Gy/onGCXin18IlMqXZ62HBwj5PBRKFYrlCl63fjchIjPsGWcnHz7EvU/EKVdM3F1uPExkS7W22zl++0kwy3DxP8z3VEVERA4KCoFERJZIMlciEphdFbN60M8ft/fW8mCbzhQ4srra/eLj1/C/Nz5GMlestZ01tedO+P6bYWo7RwKX+4Drq//U+ZExxKO7T4E/vRA2XQBjx3bVPmFXAtWz/zudL80aRC0iYlcCnbFhmNt3TrMnluWIVcGujmFXAs2x7z648wo49y9gZFMfzlZERGT5UwgkIrJEEtnirFYwqLaDJXNUKiauLn/bbYtlrUoggGcct5ovXb+dGx6d4NKTD5t7Z9OEWz4P1/4DhFfDK7/FN+/L8pM7H+fbbzsbjwGYFeuf2C7uufZHHJ+7G35xg/X5oTHYdL41TPWEF4K7TdAEpAtzQ6Cw3129rcxwd9d2InKIs2cCnbFhBHiMxybSXYdAyVyp+VDoaz8CgSG44H19OFMREZGDg0IgEZElksyViPhnX5isjvgplk2mMwVGw/6uj2ma1jplu6LmzI0jDAe9XPfg/rkhUHoCfvxOePRXcOxz4YWfg+AqbrztdiZXRfFsesqc43/7/uPZuj/JtW/aCDv+ADtugMdugPt/BNd9zPqN+hmvA9/ceUJgVwLNbgcL+mYqgURE6k1XK4FO2zAMWEPrLzxmrKtjJLJF1gw2vJ9uvQ62/Rou+ScIrurHqYqIiBwUFAKJiCwRux2s3prBAGCtie8lBEoXyhTLJiPVSiCP28VFx67mtw8dmD1L47Hr4Qdvg+w0XPqv8KS31tq6tjXZDGaLBDwk82VYtdn654zXQaViBUk3/jv88gPw+0/Bk94GZ78dwrMv1tL5MtGGrytcrQzScGgRaRTLFBgMeFg7GCDs9/S0Jj6RK84O3CtluOYjMLzReu8TERFZQTSBU0RkiSSazOmxf1vd65p4u3XCbgcDay7QdKbIHbumrYuf33wSvv4C8Efgbb+Gs99WC4DKFZMdkxmObLIZDKx19snGFfEuFxz7HHjzL+HN18DG8+D6/wv/dhL87L0wvaN213ShVAt9bHZ7WCavNfEiMlssW2Qk5MMwDDZHQ2zvJQTKlhgcqHvfuec7cOB+axi0p/uwXURE5GCmSiARkSVizamY/Ta8OlKtBOpxTbw9RLV+wPKFx0Txug2ue2A/T9r1Fbj+03Daa+HST89p23piOkuhVGm6Hh6sSqB0odx6Q8+Gc2DDFTDxKNz0H3DnN+COy+GsN8OF728xGNpqD1MlkIg0ms4Ua+9nm6MhK8zuQqFUIVssz8wEKuasIHzd6dZaeBERkRVGlUAiIkvE2tjVEALNsxLIXqc8UhcCRQJenrxllIfuvx1+/2k48UVw2X81nduzbSIFwJaxVu1g1oVUqtOa+OjR8IL/hL+6G05/Ldz6Zfj303hL8duMuLOz7hrSTCARaSGWKTBc3ey1ORriiViWXNF51aBduVjbDnbrlyHxOFz8sa42G4qIiBwqFAKJiCwB0zRJ5Utz2sH8HjfDQS8HkvOtBJp93GccG+Wdyf+k7BmAZ/9Ly8/fPm61WmyJtq4EAquVzZHBdfD8f4O/+BPlo5/Fu9w/5M/vfgnc9DnrN/LUrYgvKAQSkdlimWJtxtmWsRCmCTsnM44/P1kNrAcHPJCNwQ3/Ckc+A7Y8dSFOV0REZNlTCCQisgTShTIVkzmVQABrIoG+zgQCeEHl15zjeogbt/wfiKxp+fnbx1MMB72sCvma3m63ryU7VQI1ih5F8nlf4rn5TzI5dCJc83fwn2fAo9fVZgSlNRNIRBpM1207tAfWP1atWHTCDqwjfq81vD47bc0CEhERWaEUAomILAG7RaGxEgislrD9PVYC2euUhwfqQpzkPlbd+Anu8pzCf00/ue3nW5vBQhgt2iTs850zHNqBVL7E/eZmbnzyF+ENP4XAMHzrZQRu/S9chql2MBGZpVSukMyVaqH2pmgQoKvh0Ims9b4yak7BLV+Ak18Gh53a/5MVERE5SCgEEhFZArNaFBqsGQxwoOdKoCIhnxufp+7t/Rd/DaUct570YW7bFSOeaR3gbB9Pt5wHBDOVS11XAjFT6RPye2DzhfDWa+G452Fc+2E+6/sfslnnLR4icuiLZ633KnvGWSTgZSzi57HxLkKgamC96b7/hEoJLvq7/p+oiIjIQUQhkIjIEmhXCbRm0M94Mk+lYnZ93Fhd6wQAD/4MHrwKnvZBzjrjSZQrJr975EDLczqQzLfcDFZ/vsl8b5VAMDMDCF8IXvZ1eOoHucz4Ha975F2Qan5uIrLyTDeZcbalyzXxiWyRLcYehh+60tpSuGpz389TRETkYKIQSERkCdgtCs1mAq2OBChVTKaq8326MZ0pMBKqXjDl4vCL98Oak+Epf8mp64eJhv1c+8D+pp/72IQ9FLpzJZB9/t3IVAc/h+tXxLtccNGH+Fjgb1iXfRS+dBHsvbvrY4vIoSeetWeczQTbW8ZCtfcqJ5K5Eu/3fBe8Abjwr/t+jiIiIgebuVcfIiKy4OwWhcFmg6Hr1sRHw/6ujhvLFmfmAV33MUjth1deAW4vLuAZx63mF/fu5X9vfGzO5z64NwHAUavbVQLZ7WDdVwLZM3+CPvec224PP5VPDG/i45lPwlefDS/6bzjhhV0dP5Yp8ODeJOceOdr1ubVyx65pDh8eYM1goG/HFBFnptN2O9hMJdDmaIipdGFu1WMLofE7uNT9J8ynfAjCYwt2riIiIgcLhUAiIkvAnqnTfDC0FTgcSOQ5cV13x41lihw+PAA7b4bbvgJP/gs4/Mza7c8/dR3fuW03H/vpA00/Pxr2sWFV6xDI73Hj97h6mgmUqs4EmlUJVBXyeXigvBne9lv4zmvgu6+H5/0/q33DoW/cvJN/+/Wj3PfRSxhoEjT14s1fu5Xnn7KOf7zspL4cT0Scm7a3HdYNup/ZEJbm9A0dQiDT5NzHPsckQ4ye+64FO08REZGDiUIgEZElMBMCNR8MDfS0Jj6WKRANAD99NwxtgIv+dtbt5x8d5b6PXUKpXGn6+QONQ6WbiAS8JHoIgex2sFCzEMjv4YlY1lpf/4afwffeAD97L3iDcOorHR1/IpWnXDGZSOU5YlWw6/Nrdr6xTJG98d6GdIvI/NiDoYdDdZVA1Zll28fTnL5hpP0Btl7H5tSdfNb7Nt7rb93mKiIispJoJpCIyBJI5oq4XQYD3rkVK2Nhux2suzXxlYpJPFvknMxvYeIRuPTT0OTCJ+z3MBz0Nf3H7+lcQTMY8PS8It5+/Lnn5J5ZEe8NWAOjtzwVfvzncP+PHB0/Vr1gPJDsT2gznsxX/60QSGQpTGcKuF0Gkbr3jCNGgrhdRue5QJUyXPdR9nvW8fvwpQt8piIiIgcPhUAiIksgmSsRCXgwDGPObT6Pi1UhX9dhRiJXpGKanLX/+xA9Fo55dr9Od5ZIwNPjivgSLgP8TSqNgn7PTAgEVhD0ym/BEefAD94KD/+y4/Fj1U1CB7oMz1o5UA2B7H+LyOKazhQZHvDOep/0eVwcMTLQOQS69cuw/z6+GX4jwYH5VwaKiIgcKhQCiYgsgWSu2LQVzLY64u+6EiiWKXKG8ShjyQfg7LdBk4CpHyIBb4+DocuE/M2Dr7DfU6sUqvGF4NXfhbWnWDOCtv227fHtSqDxVH9CG7sSaCKVp1Ix+3JMEXEuninOWg9v2zIWbr8mPrkPfvMJOPLpXGs+mcEBTT8QERGxKQQSEVkCyVyJiH/uxY1tzWCg60qg6UyBN3iuoeQNw6mvmu8ptjSfSqBmrWBgDYbOlypzZxUFBuG1P4Do0XDlq62B1y3EqkNk+1YJVJ3JVCybtYBJRBbPdKbASJMNYJujIR6bSLUOZ3/1d1DKw6X/SiJXajqAX0REZKVSCCQisgTsdrBW1gz6ux4MnZl8gktdf2TymJc3nQXULz2HQIVS0/XwACG/u3qf8twbg6vgdT+GwcPhipfBE7c3PYbdDjbep/at+oqifh1TRJybblEJtDkaIlessK/Ze+T238F934fz3wOjR5LIlRhUCCQiIlKjEEhEZAkkckUGB1pfmKyOBBhPWtuunFr10BW4qZA/zfla9V702g6WypdbVwJVP55ubAmzhcfgDVdBaBS+8WLYd9+sm8sVk0Suv4Oh6yuK+nVMEXEunikw3KQSaEvU2hA2Zy5QKQ8/fx+MbIbz30OpXCGVL6kdTEREpI5CIBGRJeCkEqhiwmTaYQVKqcDGx77L7yqnEll3bJ/OsrlIwEO6UG65Zr6VTL7UdD08zIRA9hr5pgbXweuvsmYFXf5CGH+kdlMyV8Ss5mV9mwmUyteeI1UCiSw+ezB0oy1jVqXjnLlAN/0HTG6FS/8VvIHanDFVAomIiMxQCCQisgQSuWLbC5PVgwGgi/k2D/yEYGGCr5cvaVth1A/2ec8Z5NxBqk0IFK62g6XyTdrB6o1stIIgwwWXvwCmtgMzrWB+j6uPM4HynLhu0PqzQiCRRZUrlskWy4yE5lYCrRn0M+B1s308NfPBqcfg+n+FE14IR18MUGtbXej3RBERkYOJQiARkUVWqZik8p0qgawQyPFcoD99kQn/EdztOwO3a2G2gtns8+52LlC6UCLUYiZQ0NehHaxe9Ch4/U+s1o+vvxDijzNdHQp95FiYiVR3bXStHEjm2TQaIuhz9y1YEhFn4tVh7M1mAhmGUR0OXa0EMk24+m/A5YFL/nnOMQbbvNeKiIisNAqBREQWWbpQwjTp2A4GOFsT/8Qd8Pit/G7oMoZD/n6dZkv2pp1El3OB7BXxzdizghxXF605AV73Q8jF4OsvIDO1B4Bj1oSpmDCVLnR1bo3KFZOpdJ7VET+rI/6+tZiJiDN2sDs8MLcSCGDLWF0I9NDP4NFr4GkfgqHDa/ex36O0HUxERGSGQiARkUVmV9C0uzCJhv0YhsOBxH/6EvjC/Mr79KZDVPttsNdKoHYr4p3MBGq07nR4zfchuY+Tf/16Rkhw9JoIMP9BzpOpPBUTxiJ+xiL+2rp4EVkcdovnSJNKILCGQ++eylDIJODqD8LqE+Gcd8y6TyJrt4OpEkhERMSmEEhEZJHNhECtL0y8bhejIV/nSqDUONz3Azj1VezL+Zq2TvSbHV51EwKVyhXypUqt7auR3SbWcSZQow3nwKu+TTC9m2/4PsXxw9aw6vkOcrZnAI1FAtamNlUCiSyqmF0J1CLY3jwWomJC+ppPQuJxeN5nwT37/c+uBNJgaBERkRkKgUREFlnSYYvC6kigcwXKHV+DcgHOfjvTmQIji1AJNDMTyHk7WLoa7oT8zWcCdVwR386Wp/Kz4/6FY4zdPOWWPyNCZt6DnMdrIZBVCTSumUAii2o603omEMDmaJjTjUcZvutLcPrrYMOT59wnYc8E0mBoERGRGoVAIiKLzEklEFhzgfa3a2sqF+HWr8KWi2DsGGKZ4iJVAnXfDpaqtnm1agcL+twYhrVGvhd3Bc7mr4334B+/m2/4/pnE1HhPx7HZIdDqagiUzJfIFrqsUhKRns20g7WoBBqEz3i/QMq/Gi75ZNP7JHMlDAMiLd53REREViKFQCIii2ymRaFTCBRo3w720M8guQfOeQfFcoVUvtRyiGo/zbSDOa8EssOdVoOhDcMg5PN03w5WFcsUuDN4PsbLL+dEYweX3vlnkJnq6VgwM1NorDoYGubfYiYizsUyBfweFwMtNgoO/eEf2eLax7fXfQgCQ03vk8gVCfs8uBZ4Y6KIiMjBRCGQiMgiczIYGqwqlMlUnlK50vwOf/wSDG+Eo58181vz0MJXAvk8LvweV3eVQLUQqPkFHVjVQD21gwGxbLUK6rjn8pHg3zKW2w5ffz6kJ3o63ngyz2DAQ8DrZqwaAs132LSIODedKbSubNx6Hdz6ZX4WfBHXZY9teYxEtqRWMBERkQYKgUREFpkdnnQaVrp6MEDFhMlm68733Qu7boKz3wYuN/Fs+yGq/RYJeEl0EQLVZgK1GAwNVqtYqpvtYHVimSJD1Yu97cPn8anhj8LkVvjacyG5v+vjHUjmWT0YAKzZTKBKIJHFFMsUm7eCZabgJ++CseO4adM72W6viW8ikSt2bLsVERFZaRQCiYgssmSuiMdlEPC2fwteUw0h9jcbDv3HL4I3CKe/FqgborpIv/UeDHi6GwxdaN8OZt/W60ygeLZYC8BWDwa4rnAivOZ7ENtlBUGJvV0d70Ayz1jYqgCaqQRSCCSyWOqD3Vl+8X5Ij8OLvsiGNaNMpPK1FttGiWxRlUAiIiINFAKJiCwy+7fThtF+TsWaQSt8mDMXKDMF934PTnk5DIwAMF2tFlqM7WAAkYFuK4HaD4YGq1UsPY+ZQHYANhb2W1U7my+E1/4Qknvha5dC/HHHxxtP5lld/f6Phny4XYYqgUQWUdNth/d+H+77ATz1g7DuNDZHQwDsaFENlMyVtB5eRESkgUIgEZFFlsyVOs4DgjaVQHd8HUo5OPvttQ/Fsu3XKfdb15VA1RAo2GYmkDUYuvtKoErFrFYCWV/76kE/mULZOtbGc+F1P7ZmA339+Y6GRZumyYFkrlYJ5HIZRMM+zQQSWUSxbHH2jLPEHvj5++Dws+D89wCwpRoCbR9vHgIlckUGB9QOJiIiUk8hkIhIF0zTZF98fmGAFQJ1vjAZDfkwjIY2pHIJbv0KbLoA1pxY+3AsY88EWpwQKBLwdDkY2qrwaV8J5Km1jXUjmStRMWfmIdnhTa1y54gnVVvDdsMP3waV9tVGqXyJXLFSqwQCqyVsoSuBiuUKk6mVV22UyBV7Ggh+IJGjUjH7dh6F0sr8/i+Epi2sXTBNk1imwJC97dA0rTlApTy86Ivgtt5HNowGcRnwx8cmuWPX9Jx/YpmiKoFEREQaKAQSEenCLduneMqnfs328VTPx0g6HFbqcbsYC/vZF8/OfPCRqyG+e1YVEFgzgTwuo23I0k8Rv7e7FfGFEoYBA942lUB+T0/tYDF7KPbATCUQWCFBzYYnw6WftrYK/faTbY9nh272LCCwhkMv9Eygy2/eyTM++/u+BhsHg3dcfjsf/vF9XX3OdLrA+Z/+LT+/t7tZT+38zw3bedb/u37Fff/77e7dMc75p1/z8L5kz8fIFMoUyyYjdqh921dg26/hWf8I0aNq9/N73GyOhvj2n3bz4s/fNOefVL406+dYREREQDWyIiJd2D2VoWLCg3uTbBkL93SMZK7EEauCju67YVWQnZOZmQ/88YswuB6OvXTW/WIZazBypzlD/dJ9JVCJkK/9HKRQjyviY5nZrXC1bV6NVR1nvRn23AU3fAYOOxVOeGHT49kVP/ZxwKouuu+JeNfn1o1dk2limSLJXImhRaroWg52TWXIFLsL/56IZSmUKuyaynS+s0Pbx9NMpgvsS+RYNzzQt+OuNDurz8nuqQzHro30dIzpTN2Ms333wa/+Do58BjzprXPu+7U3nc22FqG822Vw1sZVPZ2DiIjIoUohkIhIF+LV2Ts7JluvJe7EaTsYwKZoiBseHbf+Y/8DsOMGuPijtXYIWyxTWLRWMLBWxGcKZUrlCh5356LSdL5EqM08ILAqgbLFMuWKidvlPMxqnIdU2+bVOFAb4NL/C/vvhx/9OUSPgdXHz7nLgVoIVFcJNOhnIpXv+ty6Yb+24tniigqB7K+7G3bAl+jhczsdc8dkWiHQPNS/jntlB7urfAX43hshMGy1gTUJkY9YFXQcqouIiIjawUREumK3HrXaRuNEIud8TsWm0SD7E3kyhRL86UvgCcAZb5hzP2uTzmKGQFYI5XSQc7pQbrseHmbmBWW6nAtkz0Oy54cMD3jxuo25lUAAHj+84hvgD8OVr4bs9Jy7jDdpBxuL+KmYMJleuJYwO8yyX2MrQbFcIZUvMZ7MY5rO27Ds58gOC/rBPuaOif5VF61E8erPY2zeIZDJaXd/HKa2wUu+DOGxPp2hiIjIyqYQSESkC/ZF56wWrS5UKiapvPNKoI2j1vabx/fugXu+Aye/FIJz2xvsdrDFYp+/05awdL7UcV6RHRJ1Oxco3lAJZG3z8jevBAIYXAcvvxxiu+AHcwdFH0jm8LldDA3MhGp2VdBCDoe2X1v9DDaWO/u5K5QrJLLOw79aCNTHwMw+5s55VPnJzOvXDoN6MZ0p8DL374lu/7G1Dn7zBX06OxEREVEIJCLShfm2g6ULJUwTxyHQ5uoK5Mrt34RiBs5+R9P7xTLF2mDkxWCvuE84HA6dzpcI+jq1g1m3d7smfjpdDYEaQpu2K903PBme8y+w9Vr43T/Pumk8kWcs4p81v2isOh9oIYdDJ/rQRnOwqf9ax1PON0rZgU2/vlflislUeqYdTHrXj3Yw88ADfNzzNQobzocL39+vUxMREREUAomIdMW+sDmQzPc0xNiunHHaDrZxNIiLCoc9cgVsOBcOO6Xp/aYzBUZCi1cJNNhlJVAqX+5cCeSzK4G6bAfLFoj4PbNmEzla6X7WW+D018L1/xceuKr24fFUfs5GoUWpBKq1g62cEKi+6qmbgM1u9etX1dRkOo+9FEztYPMz79dxIc35d7yfFAPw4i+Dq314LCIiIt1RCCQi0oVYplibTdpLS5gdmkQchkCRgJcXBO9jKPf4nLXwtlyxTL5UmdW+tNAGq4/lNATKFEodZwLV2sG6nAkUz8wdpDwWCXQObAwDLv0MHH4m/PjP4cBDgDVQujEEGlvgEMg0zZkKinm00Rxs4nXtXN18b/tdCWQfb/3IADun0loTPw/x+bY1/uKvGcnu4IO8G9/wYX08MxEREQGFQCIiXYllCxy92loN30vbiN0+5bQdDOCNnmuYdEXh+Oc3vX3WOuVFYp+/0+1MVjtYpxDIXb1vdzOBYtninM1oqyN+pjIFiuVK+0/2BuDl3wDvQHVQdIzxVH7WZjCAgNdNJOBZsBAolS9RrgYPK3EmEHQXAk0sUAh09qZV5IoV9rdrJZS25tUOdte34K4ruDb6Oh4aOKPPZyYiIiKgEEhEpCvxTJFT1w8DvYVAyW5DoIlHOa1wB983ngnu5pU+9kycxd0OZlcCObvQS+VLhB2siIce2sEyBYYHZgdgYxE/pgmTKQdVNUOHVwdF76Tyg7cxnc7NqQQCB3OG5qE++FlJM4Hqv+6m29xasEObTKFMvtRdaNjueGdtsoauqyWsd/aw7q5fx+MPw8/fBxvP5zuh1zASWrz3MxERkZVEIZCIiEPlikkiV2Ld8ADRsJ+dPVwodtsOxp++RNnw8qX0hWQLzS927YuuxpaohdTNdrBSuUKuWHG8Ir7bwdCx7Nx2sK5n+Gx8Cjz7U7i2XsP/8fyQ1dVB0PXGIm02js1T/QXzSpwJtGbQwQynqlyxTDJf4rAh6znqR2hmB1BP2jQCaEPYfMxsueuirTE9Cd9+JXiD8JIvM5Utzwl2RUREpD8UAomIOJSoW0W+aTTIYz21g9mDoR1UAhUycNe32bP+2UwyxK6p5qGTfdG1mO1gXreLgNdF0kFgkyla4ZXTFfGZLmcCxTLFOVVQdiVPV5U7T3orU0e/jL/y/JATE9fPuXl1JNBVtUo37CDD6zZqM1VWgni2SCTgYe3QgOMQyL7fUdW2zH58vyaSBUI+N1vGwvjcrp5+tmVmPpnXbRDPFp3NVirl4TuvgfgT8Kpvw+Bh1rbDRQy1RUREVhKFQCIiDsXrQ6BoqKdqgZl2MAcXOA9eBYUkhVNeD8BjE80fbylmAoH1NThpB7PbuzrNBAp67RXxztt7KhWzaTvY6sEeVrobBnee8hHurmzhpD99wGpPqT9mtRLINPs/NNgO8o4YCa6odrB4tsjQgJexsPNKIDuIq4VAfaoEGov4cbsMjlg10FOVn8w8F0eMBKmYkOoU6JomXPWXsOtmeNEX4IizAauKaLHfz0RERFYKhUAiIg7ZbTpDA1Yl0P5EvuuqlWSuhMdlEPA6ePu985uwagvRE58GtG5RsQOExf7NeSTgqVU2tWOHQKEOM4FcLoOgz93VTKBUoUTFnPu1R8PWBWS3g5z3Z+DPCu8Bjx+ufA3k4rXbxiJ+ssUy6RZtefNht/RtGA3W/rwSxDIFhoNexiJ+JpzMb2LmOT16daR6jD6EQMmZOVCbo6Ge5n3JzHOxYTQIOKjS+v2/wD3fgaf/PZz0EsAKduNNhr2LiIhIfygEEhFxyJ5xMTTgY1M0BHS/Jj6Zs9pfDHvPfCtT22HHDXDaaxgK+lgV8rGjxWPFMgUCXhcBb/uQpd+sSiAnIZCzdjCwWsK6Cdbsi8yhgdkXjH6Pm+Ggt+tBzgeSOfYySuWlX4fpx+CH74CKtWFs9WC1xSzR/+HQ9sXzxlXBFbUdLJYtMjzgYyziZyqdr21Ia6cWAq0J144xX+PJfC0E2jgaYudkZkEqvg519nvkxlXVEKjdc3PPd+F3/wynvhoueH/tw8mcHeyqEkhERGQhKAQSEXFoVjvYqB0CdVcxkMyVnLWC3fUtMFxw6qsA2DQaZEeLdjBrJs7iXzANBjx9bQcDCPncXbWDzVRBzf36u2kxso0n86wK+fAeeQFc8k/wyNXwm49Xjxeo3affEtkifo+LNUMB8qUKuWL/q42Wo1o7WMRPxYTJdOfv7Xgyj2HAlmoQ25d2sGSesbAVAm0aDZItlrtrJRRg5rnYWH1/bBlo7rwJfvIXsPF8eP6/Q10oPtPeqkogERGRhaAQSETEoXhdO9jGarvDY13ODrFCoA5hSKUMd30bjny6tb4c2DTaegbRdKY4pxJmMUQCHkeVQPa2L6eVQN20g7W7YFw96O/6Qv5AXRjA2W+HM98If/h/cOO/z1QCLUA4EKs+h/bzuFLmAsUz1mY3+3vuJGAbT+VZFfQxEvRhGBDvZgtVE7limUSuVKsEsqv8Ws3gktZitRAoWP3vJs/N5Dar1XJ4A7ziG+CZHeDaP9NqBxMREVkYCoFERByK1bUeRQJeomFfD5VARQY7VQJt/x0kHofTX1v70MbREHviuaYVIks1RDXidzgYuuBsJpB1H09XK+Jj2dbzkFZHAj1VAtlhD4YBz/0snPgiuPYjHL7tO7X79Fssa83GsQdcr4SWMNM0q+1g3loA4ygEqrZuuVwGQwPeebeDTVQHTddCoB6r/GSmPbMWAjW+jjNT8K2XW39+9XchuGrOMWZ+ptUOJiIishAUAomIOBTPFgn7PXjd1lvnxtHuB8gmsg4qge78JgyMwLGX1j60KWpdVDVbEx/LFhkJLf5vzQcHPCSy/Z0JFO56JtDMnKZGYxGrEqib2S71bUEAuNzwoi/BUc8keM37ucxz84JUAsWrs3HsMGslVAKlC2XKFZOhAS+rewiBwApk5/u9sh/TPua64QG8bqPlDC5pLZ4t4nYZrB9pMhOomIVvvwpiu+CV34LRI5seI7ZE2w5FRERWCoVAIiIOxRrarjaOBtnRdTtYsf1MoMwUPPRzOPnl1oaqKrs6odlcoFim0DQEWWiRgJdssUyxXGl7v9pMIAchkLUdrPuZQM3a4VZH/BRKFUcbzMCqTBlP5hkb9M++weODl1+OsfEp/Kvn84zu+a3j83MqlikyWNcOFptni9PBIFbX9hO128FSDkOg6v2HB7zzrpqqhUDVmU/WmvjWM7iktVi2wGDAQ8Drxu9xzYRAlTL88G2w+xZ40Rdh47ktjzGdrlYCLUGLq4iIyEqgEEhExKF4tjArbNg8GmJfIke2i5XhHWcC3fcDKOdntYJBXQjUUHlkmmZ1MPTSzAQCSHUIWWohkIPtZeEe2sFCPjc+z9z/nc20GDnb5hXPFimUK6yOBObe6AvCq65kh2czb3j8I/DYDY7P0eljDwfrQqAVUAk0E+D5GPC5ifg9HSuBTNNkPFVXCRT0zft7Nd7QDgbWz5sqgboXyxRrbVzDQa8V9JkmXP0BePCncMk/w0kvbn+MbBHDgEGFQCIiIgtCIZCIiEP2hbpto70mfspZxUClYpIqlBhsFwLd+Q1YewocdsqsDw8FvYwEvXMuTFP5EqWKuTQzgaoVTZ2GQ6fyZUI+Ny6X0fZ+UF0R300IVHfR2ci+qD+QcNa+dSA5NwyYJTDIfx3+afYaa+Dbr4Qnbnd8np3Eq7Nx7NdXYgWEQIns7CqusUjnbW6JXIlCqTKrHWy+3yv7MUfDM68jexC71sR3x972BjA84LMqgW78N7j1f+ApfwnnvrPjMWKZAoMBL24H7xciIiLSPYVAIiIONbaDba61aDmrGEgVSpgmrdvB9t0Le++eUwVk29hkQ1itmmIJK4ESHYZDZwolQg5awaC6HaxQplJxdvEdyxRabhGqzZlx0GIEM2HA6lYhEDAwvJp38PcQHIVvvgQOPOjo2O3kS2UyhTJDA17Cfg9ul7EiBkM3DvWOhjuHQPYQ5+isdrD5tc6NJ/OsCvlqs77AmsGVKZQXZAj4oaw+BBoa8HLyxNVw3UfhpJfCxR93dIzpJapsFBERWSkUAomIOBRrqATaUN2A43SLkF0x07Id7M4rwO2Dk1/W9ObN0dCcwGl6CYeo2l9H50qgLkIgn9UylmmyBa2Zxuek3li1rct5JVCu+nmtQ6DVET8PZSIUX/tjcPvh8stg6jFHx28lXheGGIa98Wp5zgTKFctc/8h4X45lB1328zcW8XcM7BqHOA8HrcHQTkPDZiZSeaLh2T8/G2vtl/NrCbt95zSTDkPIpfLAngS7mwyc74VVmWc9n0/mbv4s/v9g84Vw2efB5eyvnFawq6HQIiIiC0UhkIiIA6ZpEs8UZw1gHhrwsirkc7whzF6n3rQSqJSHe74Dxz236dpksAZR74lnZ62Jty+kl+I354O1drD2VSvpfMnRenigFhalHbaExTKF2lr1uefnwedx9bUSyA4fJr3r4PU/tuY3Xf5CSOxx9BjN1Nqi7FkqfRh2vFCuvm8vr//qn3gilp33sWrhV/X5c9IO1hgCDQ14qZhWlV2v6reN2Ta3GcTulGmavPbLf+TLf5hfSLjQ3n3lnfzz1fOvaIOZtkb23MU7D3yUx4z18Ipvzhpy30l9kCQiIiL9pxBIRMSBXLFCoVyZs4VqUxcbwtpWAj18NWSnWraCgVUJZJrM+q39dN2GpcXmtBIoXSgT8jmrBAp3GQLFs8WWrXCGYbA64udAwtlg6AOJPAGvq+0qe3to9IFkDlYfD6/9gbXR7fLLID3p6HEaNW44G+zD2vOFMlXd3DSdnn+lUixbwOd2EfBafxUZi/hJ5kqzQs5GM5u8ZkIggPg8QrPx1My2Mdu64QAel+E44G0mXSiTLZaZWOYtZXtjWfbGnf2MtFOumCRyRdYbE3DFy8h7hnhL6YMQGOrqONOZgtbDi4iILCCFQCIiDtjtOY1hi7VFqNtKoCYhw11XwODhsOWilp/frEVlpqVmKQdDO6kEchYCBavtYE7WxNub0dqtkl7toMXINp7KszoSwDBaD6SdM2z68DPh1VdCbCd888WQSzh6rHq159AeqBtcviGQvQmumw1urcQzVoBnf7/tIKZdNdB4Ko/XbcwMH66+7nutnDJNs2klkMftstbEzyMEsp/D5fpcAmQLZdKFcm3W0nwkc0V8ZoEXPfpBKOX42cn/wa7iEPmS8+2JYL0uVAkkIiKycBQCiYg40HihbtsUDbE3nmtbvWCbqQRquMBJ7IGt18GprwJX67apTU1mELU6r8XguBKoixDIrsJxEjKkC2VKFbPtBeNYxO98JlBibhjQqOmw6U3nw8svh/33WVvDCt3NV4k3DEhezu1g6WrbldNKrXZqrUNV9ve+XSAxnswTDftrm+bsMKjXGUqpfIlcsdL0ee+myq8ZuzppOYdA9vd6Ijn/yq54psAnvV8lmnwQXvRFKmPHWR/v4usvlisk86WWLZ4iIiIyfwqBREQciDess7ZtrAYzuxwMVk1Uw5LBgYZA5O5vg1mB017d9vOHgz6Gg14eq5tTMp0pEPF78LgX/+3c63Yx4HWT7BAIpPJlwgswE8huSWpXBbU6EuiyEqh9CGRvpZoTLB1zCbz4S7DzJvju66Hk/KI61vDaGlrG7WDJPlYCNW7bs4OYtpVADVU7dnDW6/erccZQvY3zXBN/MFQC2T8b2WJ53sGe+/av8FL39Ww/4V1w3KU9terZ36uRkCqBREREFopCIBERB1qtYt9UbdF6zMEAWXsA8GBjJdC934cN58LokR2PYV2Y1reDFRhewgumSMBT+7payRRKjmcC1UIgB4N+ZwYLt68EimWKjlpSDiRyHSuBfB4XI0Ev46kmM1ROegk8/99g67Xwo7dDxVkbTDxTwDBmKsSGgj4SuSLleWy8Wih2UNCXEKhhs9tYsyqrBuPJ2fN77Oe+18qpmRlDgTm3bY6GSBfKjkPERvFqddKyDoHqArdOQ7nb2nkz6275ONeVT2fqSe8B6p6bLr7+WKZzsCsiIiLzoxBIRMSBeLb5xYkdAjlZE5/MlfC6Dfyeurfe6Z1w4AE47nmOzmPzaHBW4BTLFpe0dSIS8LRtB6tUTDKFMkGnK+L9zmcCOZmHtNpBdQlYq88TuVLHSiDrmIHWLWZnvhGe+Y9w/4/gmg93PBZYIcFgwIu72uI0PODFNDvPWloKdviT6tAC6EQiO3vb3qqQD8PoPBOoPqgbHJhnJVCqXSWQ3X7ZW0vYwVAJVN961/NcoMRe+N4byAQP573FdzIcsr6XtSqtLgK66SVsbxUREVkpFAKJiDjQqh1sKOhlJOjlMQezQ5K5IpGAd/bg4Uevsf59zLMdncfG0RB74tlaZcv0Eg9RjQS8JNqEFXZFz0K0g7Ua1l3PSYtR/e329q92xjoNmz7v3XDOn8Et/wW3f63j8WLZ2W1RQ/MMNhaSHQL1YyZQLFOY9XV73S5WBX0tn6tyxWSyIQQKeN0EvK4FaQfrpsqvGfucMoUyxXKlp2MstPpZQD2FQKW81f6YT/Gb0z5LglAtmBvqqRKo2g6mSiAREZEFoxBIRMSBWKaIx2UQ8s0NMzZFQ44rgeZsBnv4ahg9CqJHOTqPTdFgdU18FrBaiZbygqlTJVCmYIVVTgdD221jTtrBnAzFnlnp3v4C90CbMGDuMR0Mm37WJ+Goi+Hn74PHrm9711hDkGf/eTkOh7YrgDrNgeqkWK6QLpTnBHhjEX/LEGgqXaBizn2Ohgd8tTaibo0n83hcRtPX0PqRATwuw9HPdjP1wdRyDPTACn6qBWiMp3r4Hl79AXj8T3DZf7HTtRGYCX/sCsVunpvpTOdgV0REROZHIZCIiAP2/JJm68M3NczpacWqBKoLQ/Ip2HGD4yog+7EAdlSrE5a6Emgw4G3btmRXjjidCeR2GQS8LkeVJrXqrDZf/+rB7iqBnIRAdlDRdmCw2wMv/aoV8H3ndTCxteVd4w2VQLUQaBkGB/3aDta4Ec3WrsrKrlSpnwlkH2M+M4Hqt43V87hdrB8ZYMc828Ea/7ycjCfzbBwNdWzDa+qOy+H2/4Xz/gpOfBHxbJGgz43fYwXlkYAHw6DjzLB6MYVAIiIiC04hkIiIA/Fssdbm0GjjaJA98WzHNfHJXImIv+4Y238H5YK1WcqhWgg0maZcMUnkiks6RLVTJZAdFjitBAJrTXzKwUyg6XRh1kVnM6PVOTOdKoHGk9agZyczgcYifgrlColshyAkMASv/g643PDtV0Bmqund4tnZz6E9J2c5BgepPm0Hqw1ab/iZGgu3rgRqFdQNzmObWuOMoUYbR0O1wLVb8brXx3J8LsEK1tYOBhgJ+rprB3vidqvKbcvT4OkfAez5ZDPPp8tlMDTg7bodzOMyCHfxfiEiIiLdUQgkIuJAPFNs2Xa0ORqqtmi1rxiY0w72yNXgH7I2gzk0EvIxNOBlx2SaRLaIacLIks4Eah8C1SqBHM4Esu7rcTgTqPVzYvO4XYyGWs+ZsY0nrbaY0bCDdrBBu8WsyYawRiOb4BVXQGwXfO8NUJ57QWzNxpl5Xcys1u6txWkh1QZDOwjp2rEHrTeGQNE2VVb2cxhtrASaTwiUbB8CbY5aVX69rImPZQq1VqtuhiMvpolUnmjETzTsY8JpJVBq3KpuC6+Fl3zVqnrDCnAag/Khge6qtOzKxmYVlyIiItIfCoFERByIZQstK242Ohwgaw+GBqBSgUeugaOeAe7uQpxNo0F2TmaWxfyMSMBLtth68G2mGhZ085v9kM9DxuFMoCEHVVDRsL9W6dPKgWSeVSF/bUNXO3Y7kuP2mY3nwgv+05oN9Iv3Q12gUKmYViXQQH0l0PKcCVQsV8iXrOc5Nc/NZTPtYLOfv7Gwn3yp0nTmUKtNXvNpB5tI5YmGW7+GNo4GSeVLTKa7D+QS2SKHjwwAy7kSqEA07LN+RpxUApVL8P03QWYSXvlNCI3Wbkpk57amdhvQxTKt32dFRESkPxQCiYg4EMsU51Qt2DY5XCU9qxJo752QPtDVPCDbxtEQOybTM+uUl7gdDFqvDLdnyAQdzgQCq2rISbtRPFtwtEp69WDA0WBoJ61g1vH8tc9x7NRXwvnvtbaF3fKF2odThRIVc3aQ5/O4CPncy24mUH11VnqelUCthnq32+Y2nswT9LnntBYO9VgJVKmYTKQKbSuBGmdwdSOeLbJhVbD25+UmWyiTypcYi/gZi/idtYNd9w/WHLPn/zscduqsm2LZwqwwE2Ao6Ou6HWwpKxtFRERWAoVAIiIONA7vrTcc9DEctFq0WqlUTFKFEoN2CPTIr8BwwdHP7PpcNkVDPDGdrV0oL+12MOt70qolzA5zuqoE8nschQyNW7VaaTdnxtapLWjW8RyunZ/j6R+G458P1/wdPHodMNMm1KyNZrkFB/ZzbBgLOBOoQwjU7DkaDvrIFssdZ3I1ms4UKFfMOYOm622K2jO4uh8OHc8WOWJk+YZAdugTDfuJhv2z1sU3de/34ebPwdlvt0LNBs2C8qEBb1dtjdOZQm0mloiIiCwMhUAiIh2UyhWSuVLbwMGuzmklVShhmnUX+w9fDUecA8FVXZ/PptEgFRPufSIGtF+RvtDsUCvRoj0o3etMICftYFlnQ7FXD1ohUKXSeq7LgWTOcSVQxO8h4HU5mwlUz+WCF30RVp8I338zTGydaYtqvHgO+pZdO5j9nIyF/W03wjlhV4c0hl8dQ6AmgY0dPHSzhQrq28sCLe+zfmQAt8vouhLIbvMbDfsI+tzLMgQar9u2Fg37yRbLrWdx7bsPfvIua37Zsz7Z9C6xvrSDqRJIRERkoSkEEhHpIFGtgGhVCQSweTTIjonW1QL2BWok4IHEHth3T1dbwerZ1Ql37Y4By6MSqHUIZFVndNUO5nN3HAxtmmZ1foizSqBSxWzZlmK3BdltXp0YhlFbE981XwheeYU1TPfKV5GMWRvDGsMs6+J5eQ2Gtlv+1g4FSBfKPQ1LtiWyRQYDnjkzmOyQp1lrUqtNXrVB2t2GQC22jdXz1tbEdxcC2W1+QwPeZVnVBdQGQUfD/vbVbdlp+M5rrG13L/s6eOa+3+SKZQqlCkONIVDQ+trbBbD1YtkCIyFVAomIiCwkhUAiIh3MDLFtXwnUbk283UoTCXitVjCAY57T0/nYc0ru2R3HZTB749gisx+7VTtYOl9iwOt2NHDZ5qQdLFMoUyybDmcC2TN8mlfuTDloC5pzzEjnOUMtjWyEl18OU9vZcsNf4aLSvI1mmQUH9rDmNYMByhWTXLH5MHAnYpnCnMAArK/b6za6bAerDtJegBAIrJ/tTvO+GsVrM498XW/IWiz1g7bt4dhzwrdKGX7wNog/Aa/4BkTWND1Wq/a+oQEvFZOmg74b5YplcsW5PwsiIiLSXwqBREQ6iNlbuNrMqtgUDWKa8Ph084vFmRDIA4/8EoY3wtixPZ3PSNBrrWbPlxga8OLqImDpt8EOM4HShdKcQb6dhKvtYO0qTWIOgjnb6mq7T6vKHfvj9up3J1ZH/L2HQACbzofn/Atr9v2e93q+N7eNZh4brxaKXZ21tvp9ms9coFjDRjSby2VUt7nN/t7mS2Xi2WLToM4+TrffL6ch0KbRIDsm0l1VPsXr2t2GBrxdt6otBnsG0Gh1Oxg0CYF+9ynYei0851/giLNbHiuWbf4e2U2rnv38LWVlo4iIyEqgEEhEpAM7cGhWuWCb2SLUKgSqXhR6SrD9d9ZWMKO38MYwDDZXW8KW+oJpphKodTtYuIt5QGBVApkmZNsM+rWDOSdDZO2L/AOJ5qHNAYdhQOMxe2oHq3fWW7j/sBfzLs9PGHnsp7NuGgp6l912sPp2MJhfCBRvMj/GNhaZu658IlWo3QaAacLUY1DI1IKGWBcDiMEKgQa8bkK+9q/PTaMhkvkSU12sibdDj2XdDpbKMxz04nW7ZtrBUnVf40M/h+s/Dae9Fs56c9tj1Sqf5oSZzgO66erzp5lAIiIiC2vpeghERA4S9Rd0rdRCoBazQ+xKmdUTf4RSDo7tfjV8vY2jIe55PO6oEmYhhR20g3UzDwioXZSn2nxuq4vOZlbXLnA7VAJ1EQKtjviJZ4vkimUC3u5CrhrD4KeHv4fcnvs586d/CauPqa3dHhrwUihV5nf8PkvVtYMBHec2tRPPFFk3PND0trGwn32J2a171nNksrm0Da77X3jgxzC1HVxe1q09jQ941hLZPQEnXWbNrnHAnjFkdAhjN0WtDV87JtOMOmwZjB8EIdB4Ml+rAFoV8mEYddVyE4/CD98B606H536mY2Ada/EeWQvoHMy3skOgdmG7iIiIzJ8qgUREOojV5nu0vjgZCVmzP1qHQNVj7P41+MKw8bx5ndPmUevC1Ml2rIXkdbsY8LpbVgKl8qWu1sMDtfaxdnOBprtoHQn5PQR97jaVQFbg0G0lEDQfYNyNqZzJ3/v+BoKjcOVrIDUO9N7itJBmQiDra28V/DlhtYO1qQSywwjThL13M3TjP/Fb33s555rL4MZ/h+EN8JxPw7l/gdtl8Bb3L3j23e+GT22E/z4ffvkhGH+k7Tm0mjHUaGOHKr9m4nXVg8s1BJpIzWxb87pdjAR91us5n7Reix4fvPwb4O3cJhlvMROoNq/Jwes4rnYwERGRRaFKIBGRDloNPW20qc2GMGvDmEngsWvhyIvA4zxwaMa+MF3qSiCwWsLazQTqZuAy1IdArUOG2gwSh1+/NcOn+WDoA4k8Yb+nq4ole87QgWSe9SNBx5/XKJ4tUgmuhldcAV+9BL77OnjtD+uGHRdq7VdLzR7ybf8c9FoJZJpmx3awyXSBcmIf7m+/AvbexUbDzR/MExi5+K8ZPv3FEBqt3d8Anvqxn/K2LVO8+Yi9sPMmuO2rcMsX4MTL4IL3w9qT5jzOeDLPkWPhjud7xEgQlwE7u9gQVptZNeBlOOglWyyTL5Xxe5ZHVRdYIdDJ64dr/x0N+5hI5ODH74TJR+F1P4bhIxwdq9Xw/OEuNrdNd1HdJyIiIr1TJZCISAfxbJGw34PH3f4tc+NoqG072CnuXRjJvT1vBatnt6gsh9+atwuBMvlyT4OhoUMI5DCYs7Wb4dNq9Xin40HrYdNOxTJF62tYdxpc9gXYdQtc+WpGvFYVVHyZVQKF/J7a89nrTKBUvkS5YrZ87sYiflZVpjC/9lyrLem5n+HL5/yK1xc/RPDJb5kVANn8wTB3eU6Bp30Q3nAVvOd+OP898Oh18N/nwbdfBU/cPutznD7vPo+Lw0cGeKyLDWHxbBGPyyDoc/e8wn6hTaQKta1gYK2Kv+DAN+HBq+CZH4ctT3V8rFi2gNtlzKn6G+wqBLJnAi39e5qIiMihTJVAIiIdxLIFR2HD5miIq+7ew5F/+4s5t5UrJh8I3gUVA45+5rzPyZ5BtCq09BdMQwNefn7vXn7Z4us+Z8uqro5nhwztLhzj2SIBr8vxvJzVgwF+fs/els/NOZu7O0d7ftC8NoRhfR1HrKpWEp30Yihm4Sd/wUmFd+LnTfMeDv2aL9/C805Zx6vO3jCv4wCk8mUiAQ+ReYZAsbr16c0c7o5zpe8TuBJxeN0PYONT2P3j+xgOevF5mgexQ0Hf7O9VKAoX/wOc927445fgls/D/zwdjnwGXPjXFA4/h1imWJuJ08mm0VBXlUDxrBXuGYZRC0IS2WKtgqxXN2+b5MM/uY+r3nVe17O26mULZVL50qyv/3zXPbw6fTmc+GI4911dHc8OMxvnKwW8bgJel6Oh3bFMoaufaREREemNQiARkQ7imdatK/XsC+1ypfkq6Vc99CAEz4Tw6nmf02jYz+defTpndxleLIQPPPs4bnh0oulthgEvPG1dV8c7enUYr9vgjl0xnnXi2qb3iWUKLUOEZt510VFsrgZnzVx03FhX52gP0p3oQwh0cn3AePprwKwQuepdfMEbZyp5Ss/HTudL3Lh1kjWRQH9CoFyRkN8970qgeLtte4m9POUPb6BsTHPP077GaRufAlTn97QJbIYHvM2DhoEReNoH4Nx3wq1fhps+B//7bCpHPpvNxjMZi5zs6JzXDgZ4dH/K0X1hJgQC+loJdPO2CbYeSPH4dJZj1kR6Po49y6pWCTW9gzfu+Ue2mYdzzAs/1/XmwnibGU/DAz5HX/sTsSzrhpoPCxcREZH+UQgkItJBrO6Crp21QwHe88xjmt+YOgA33wOn/33fzut5p3QXriyUc7aMcs6WuS06vQr5PZyxYYQbHh3ng885rul9Yg6DOdvxhw1y/GGD/TpFPPWDdOeh1g5W74zXkSsUePov38vOW/8PnPX9nmZI7Zqy2pcmulht3k46X67OTnJjGL3PBIrXzcuZJbEHvvY8/NkDvKzwAV49cDKnVW/q1Lo1NOBtX6njj1jtYWe/A/74BbzXf4ZrfNeyZ+u9cPI/QLB9mDoa9jOZzmOaZsdtYmBV/QwuQAi0035OU/l5hUD2pryxsB8KGfjOa3FT4W2F9/AL00/ruLS5eN3X22howOtoMPSuqcxMVZyIiIgsGM0EEhHpoN0QW8ce+ZX172Pmtxp+pbjwmDHu35NoGbJ0GwIthNGQj8lU7wFLvlQmWyw3/Tr857yZvy+9mY2T18P33gil7h+nFgLNs1rJlsyXCPutlp+wv/UcqE5q85zqv+74E/C150LqAPlXfp/bzWNrQQV03uQ1HPQ6a53zBeGC93HTpdfyvfJT2bD1cviP060h0m2+x9Gwj2LZJJF19jXXh3v9DIFqz+k8Xncw85qIhv3wi/fDvvu49cxPs9Nc21Ow2e7nccjBc2OaJjsnM2wcVQgkIiKy0ByFQIZhPNswjIcNw9hqGMYH29zvJYZhmIZhnNW/UxQRWVrWBd08Z+88fDUMroc1c7cUyVznHxUF4MatzdvMYtnu2sEWQjTsn1cl0Exb1NyvwzAMrvZfylWHvwce/gV8/01Q7i5E2DU5UzXSD+l8ibDfmtcS9nv6UAlU/bprAdA4vO6HDBz5FII+d23otmmajtrB4tkilRatmI2eKEb429JbOfCaX8O60+GXH4TPPxkeuAoqlTn3rw0Cd/i9rA+Oh6vPr5NqmE5qz+k8gz3761h/4Ldw1xVw4fspHWnNKutl2Hn7djAviQ4hUDxbJJkrsUGVQCIiIguuYwhkGIYb+C/gOcAJwKsMwzihyf0iwF8Bf+z3SYqILBVrnbWzwdAtFdKw7ddw3HO7nrWxUp10+BDDQW/LWUPLoRIoWl1l3qt4hw1nQwNefhV6ATzn0/DQz6wgqIuKILtqZCpdcByOtJPKlwgHrC7ysN/T+2DorPU1DAe9VrD1rZdDegJe90M44mxg9ja3dMGqmGpXCTQ44MU0rWolJ+xjD286FV73I3j198Dlhu++Dv7zDLjlvyGfrN1/NGQ9ttNArX4m0GCg86BzJ1L5Uu31NpmeXwg0kSwwSJrh33wAVp8IF/5NbUh0b5VArd8jnbSD2a9VhUAiIiILz0kl0NnAVtM0t5umWQCuBF7Y5H7/CPwLkOvj+YmILKlssUyxbM4vcNj2GyjlrBBIHHG7DM47KsoNj45jmrMDDNM0rTlNy6AdbD4VGbFWs3GqhoLVCopz3gHP/hQ8+FMrpCg6+9+sfWFdqph9aUWyV8SDNbep58HQmSI+T3UL1J++BPvvgxd9oRYAgTWrxg4j7MCmfTuYr3ZsJ8ZTeYYGvPg9biuYPeZZ8Oc3w0v/F0Jj8MsPwGeOh19+CKYeIxqxju+k/a9SMUnkZkIgj9tF2O+Z93Owe2pmRf1Ecp7tYKk8Hwt8CyM9Dpf9F3h8ddVO3R27XDFJ5EpNK9rAbtVrf8yd1QqnDWoHExERWXBOQqDDgd11//149WM1hmGcARxhmubP2x3IMIy3G4Zxm2EYt42Pj3d9siIii21mnfU8AocHf2ZtKdp4Xp/OamW48Ogo+xN5Hj0weytTrlihUKoseTvYWMRPMl8iVyz39Pl2YNEqYByur6B48p/Dcz8Lj/wSvv1Ka5hvB7umMnhcVuXZfCtHCiXre26vh48E5hEC2a1DyX3w23+Go54Jxz1v1n3qK4EchUDVn89OYYOt6YwhtwdOejG89Vp462/g2GdbIdV/nM7ma9/Gua77mUh2DuCS+RKmObvCa6jarjYfdlDicRnzfj7HDvyBF/FbOO/dVjscMxvvum0HS+bav0cOB33kipW2Pyd2YHnEiEIgERGRhTbvwdCGYbiAzwLv63Rf0zS/ZJrmWaZpnjU21t06XhGRpRDr0LLTUbkIj1wNxzzHusgUx84/2vr/xPWPzP6lwax2oiU0GqpWh/TYEhZrnI3TYDjomx1qPOktcNkX4LHfwxUvndWu1KhcMXl8OsOJ66yNaOPzrByx5//UKoF8HlLzGAw9HPTCNR+Gch6e8y9z2iTHIv7a3Jo568ybsKvCnAYtnWYMsf5MeMmX4f/cBxe+H9/eW/m275NceuNL4Y7LoZht+an2/Jv6bVmDDubidGJXAp24brDrap1Z8klete8zPOE5Ap46M+bR2+PGu1iHMNN+72z39e+azBAN+2uvLxEREVk4TkKgJ4Aj6v57ffVjtghwEvA7wzB2AE8GrtJwaBE5FMwM7+0xcNjxB8jF4fjndb6vzHL48ABHjoXmzAWaTlvPychSzwSyZ6j02BIWy1gX8u1mqcxpbzrt1VY4sesWuPwyyMaafu7eeJZi2eT0DSPWOc5zOLRd9ROuXqSHA70Pho5lCzzZ/RDc+104769g9Mg59xkL+4lliuRL5ZlKoA6DocH58OVOK+drBg+Dp/89xnvu5+OuP6dUMeGqv4TPngC//ri11r7x62tSPTjcj0qgqTRDA162jIXnNxj62n9gtDLOles+CN7ArJui4e5bHO0ws93ruP5+zeyayrBh1UBXjysiIiK9cRIC3QocbRjGZsMwfMArgavsG03TjJumGTVNc5NpmpuAW4AXmKZ524KcsYjIIorbVSe9th499DPwDMCWi/p4VivHBUeP8cfHJme1ktjVMfPe2DZP0WqI0GtrTiJbxDCs1qpmhga8JHIlyo1DnU96Cbz8cth7N3z9+ZCenPO5dnvNGRutEGiy3yGQ3+N4CPOcY2VyvD35BRjaAOe/t+l97IBmMlVgPJnH7TIYaTFzBmZCWkdr4um8cn4O7wA3Ri7lI4d9Ed7wU9hwLtzwWfi3k+H7b4EDD9XuGm8SijgZjtzJrqksG1YFiYZ9TKbzc2ZlOfLYDXDbV/iGeSmpsdPn3NzLxrvatrdWbY0OqrR2TWXYOBrq6nFFRESkNx1DINM0S8C7gF8BDwLfNU3zfsMwPm4YxgsW+gRFRJZSrR2sl6qTSgUe+jkc9QzwadZFLy44OkquWOGOndO1j3WapbNY7HawXof0xqobpFyu5hvj7K+vaRvN8c+DV10JE4/A158Hyf2zbrZbh05dP4TLgIn5tA9RFwLVbQdL50s9BREXJ3/C+uJj8Ox/bvlzURtSnMwznswTDftafp/AWcuRLZ0vkSm03zbWTDTis1r/Nl8Ir/oWvPsOOPvt8Miv4CvPtKqzaF492I+ZQLsm02xYFWQ07CdXrJAudDmLqpCGq95FZWQz/5x/aa2SrV59G55TMxVtLdoaqx9vFYIVShX2xLMcoc1gIiIii8LRTCDTNH9hmuYxpmkeaZrmJ6sf+4hpmlc1ue/TVAUkIoeKeIcNTm3tuROSe+H45/f5rFaOJ28Zxes2uL6uJSzWofJgsdgX0d1eNNtimWLbWVP2bS3Dg6Mvhld/F6Z3wOUvgMxU7aadk9ZQ6MOHB1gV8s97kLA9/6d+O1jFtLbndSW5j7eWruTRwXPbbsubFQI5aN3ye9wMeN21QKKd2oyhdjOBmhgN+WeHaau2WEHWO2+2Nop940Ww/XfNK4GC8wuBrBlPWTaMBmuvu66ru37zSZjewcRF/0oOf9PvaTTs7zrUbPb11qu1g7V4bp6IZTFN2KgQSEREZFHMezC0iMihLJYt4nUbBH3u7j/5oZ+CywPHXNL/E1shQn4PZ2wY4YZHZ4ZDz8xcWdp2sAGfm5DP7WhteDO1LVktDDtpcdryVCsImtoO33qFVe2B1V5z+MgAHreLaNg378HQdiVQpG4mUP3HnSr/6sN4KXLzMX8zZxh0vfqAzaoE6hzYDAedtVzZM4ai3VYChf3Ng5fhI+BNV8PIJrji5Qw9/htgbjtYvtR+Q1Y7e+NZShWz1g4GXc552v0nuOXz8KS38vjwmUDzECwa9pMtlrua9xTvMDy/09DunZPWa1br4UVERBaHQiARkTbsag2jzQVrSw/+DDadb62Hl55deMwY9+9J1C56Y9kCPo+LgHfp/xcWjXQ/Q8UWyxYZajfnptZG0yHA2XwBvOQr8MRt8N03QLlYHbRrXVRHw32oBGrYDhb2W6FoVxvCdtyI+77v8sXy8zBGt7S962g16LDbwZxU7QwNeB3NBHIyaLqZaMRHulAm26wNK7IG3vhzWH08z77vvTzf80cGvDPB8WAX7WrN7Kquh9+4aqYSyHGLXyENP/ozGFoPF3+0Nvi5VTuYdWznr5dYtkjI58bnaf7zGPF7cBmtQyC7dXGDKoFEREQWxdL/DVpEZBlLZNu37LQ0/jBMPgrHaSvYfF1wdBSAG7daLWGxdJGRYI/BXJ+Nhnw9ByzxTGF+7WD1TngBPO//wdZr4Sd/we7JVF0I1P3a70bpOTOBvNWPO6xsKRfhF++nGFnP50svbBt+gdXeNRz0sj+RY8LhJi+nc3fGHaycbyYa6hCQBFfBG65i98AJ/Jvnfo0XSQAAfVFJREFUPzHu/nbtpmEHG7LasQd9HzErBHL4nP7yQ1al2GVfAH+kFh5FI3Ofg2hd+OZUp7ZGl8tgsM1g7J2TGfweF6u7fD5ERESkNwqBRETaiGULDHe4YG3qwZ9a/24z90ScOXHdECNBL9c/Ug2BsoUlbwWz9TJDxea0HczxLJkz3whP/zDc8x3eVfwaG0aslduj8zhHW9KeCeSzZwJZVS7JvMNz+9P/wIEH2HnWh8nhdxSsjoX9PHogRaliOgpshoPeWmtSO+PJPC4DVoW6ew3ZoUnb8CUwxL8d9inucp8MP/5z6+umy0CviZ1T1oynw4YCtfN29Jw++FO44+tw3l9ZFWPMBDyjoebtYNBdJVA8W+gY6g23CejsqrXlEOqKiIisBAqBRETa6PRb7pYe+jkcfiYMruv/Sa0wbpfBeUdF+cPWcUzTtJ6TJR4KbYtGemu1qlRMKwRq83XMDNTtIji44H1MnPRm3uK5mqdNXGGdY3XOS6bQ20p3sCqBgj437uqGrkg3lUCZKfj9p+DIZ7Bz7GmAs0HrYxE/D+5N1P7cyfCAj1i2czAynswzGvbXvhanRkPO2rDG8x4+vepjcMxz4Bfvh2s/wrCvAuAopGpm11SG9dUZTz6Pi6EBb+fXXWIvXPVuOOxUuOjvah+eSOUZDnqbtm/VBnJ3MeeqU5gJMBT0tayCstbDqxVMRERksSgEEhFpI5bpfIEzR/wJ2HOHWsH66IKjo+xP5Hn0QMrRRediiYZ8TKULlCvdrUpP5ktUzNbDdAG8bhchn7u7EMgw+NMx7+fH5adw7H2fhdu/PjNIeB7VQKl8qTYPCGYqgVJOKoF+/y+QT8IlnySWtYIoJ5vdxiL+WgWSo5lADjdwOZ0x1MgeJN1pK1c8WyQYDMErvmFVZ9347xx31fM4xdjWcyXQ7qnMrBXqHVv8KhX4yTuhmIUXfxk8M5U6E6nWg7ZXhXwYBrW5QU44CcqHBrzEm8y2Mk2TXQ1fm4iIiCwshUAiIm0ksj1UnTz0c+vfWg3fN+cfPQbA9Y+MW8HcMqoEqpgwle4uYEl0WKttGw76ug4Odk7l+Ovin1Ha8gz42f/h+ClrW9XEPIZDp/Kl2mYwqN8O1qESaGIr3PplOOMNsPr4WjWI03aw2p8dzgTKFTtv4HKycr6Z0ZCzrVxxe46Y2wvP/3d4zQ/wFFP8yPcRjrr3M1Dq/nnYOTm7WmY07G9fkfTH/4Ztv4Fn/xOMHTPrJisEat6+5XW7GAn6anOTnIh1qGiD1u1gk+kCmUJZQ6FFREQWkUIgEZEWiuUKyXyp+3awh34K0WMgevTCnNgKdPjwAEeOhbjh0Qli2QIjvcxpWgB2i1C3LWG1Nfcdvg5r2HF3AdOuqQyRUBDPK78B68/mxJv+ite6r+2quqNRYyVQuPrnjtvBrv0IeAbgor8FrIDEMCAScFYJ1OzPrTidoTSe7C0ECnjdRPyeju1gVkhZ97wefTHmn9/M98tP5dQdX4UvXghP3O74ceOZIvFscVZQMhZus5Vu//1w3Ufh2EvhzDfNudn6+gMtHy8a9jl+rZim1dbYKSgfDjbf3LbT3nqmdjAREZFFoxBIRKQFu1qjq9ajzBTsuFGtYAvggqPHuHn7JLliZfnMBOqx1cqeXeOkjaardjDqWod8IXjdj8hvvphPeP+XdXd+Fszu2tZs6XypFvwADHjduIyZrWFNPXY9PPxzuOA9EF4NWBvRBgNeR/N47KAm4HXNeuxWnAxfrlRMx9vGmolG2oQvQLliksyVaivhbe7gMJ/wvJNvHvlZqzXuyxdbQY2DqqBdTVaoj7YKaoo5+MFbITAEL/hPaDJseSJVaFkJBNVh5w4rgXLFCoVSxVk7WLZIpaFtUuvhRUREFp9CIBGRFuyLya62gz3yKzDLcLxCoH678JgohZI1YHfZbAeL9FYJNPPa6lxB0X07WJqN9kW1L4jxqiv4TulpnLT1i/DTv4Jy9wOik7lSrQUMwDAMwn4PqVYhUKUCv/o7GDoCnvzO2odjWeeD1u2gZizid7Q5yn5NtAvN4tkixbLZ00wgsFrC2gUkyVzrdrehAS+3+86Ed94Mp70a/vD/HFUFzYRAodrHomE/iVyp9vNQc91H4cAD1jr4UHTOsbKFMql8qeVMILC+307bwewws9PP49CAF9O0ZmHVsyuB1o8oBBIREVksCoFERFroZn5JzUM/g8HDYd0ZC3RWK9c5m0fxuq0wYNnMBKq2g4132WpVawfrOBOoeRtNK8VyhT2x3KzKCr/Pzyc9f87v17zBWhf+3ddZA4O7kC6U5lTjhP2e2uDmOe65EvbdA8/4B/AO1D7caSNavVoI5DCwsY8bazKA2GaHGz1XAoX9TLZpB4u3ec+wq2EIDMEL/wte8wNHVUG1EGi0fjB0k/Dx/h/DH78AZ78Djr646bHsAKvd9zQa9juubHMeZlohUeN2tF1TGdYOBgh43Y4eT0REROZPIZCISAv2BYvj1qNCBrb+Go57btM2DJmfkN/DmRtHgC5b9BbQ4IAHr9voOCemkX3x3Ng2NPf4XuKZIqbDNq49sSzlijkrMACIhgN8d+iNcOm/wsNXw+UvtFoXHUrlmoRAAU/zdrBCGn79cTj8TDjpJbNucrJJymYHFU4DGyftYHYLVe/tYO0rgRyFQLajL65WBb2mbVXQrqk0oyHfrO//aLWdazJVgFLBqrr63htg3enwzI+1PD8nIVg07CdbLLdv9auyw0wn7WAwUzlk2z2VmfNaFRERkYWlEEhEpIV4tzOBtv0aSlkrBJIFcUF1S9hymQlkGAajIX/HteGNYpkCAa+rYwXE8ICPQrlCrlhpez9bs/kxYFd35OHst8HLvw577oT/fQ7Edjk6bjpfnjUYGqxQrmk72E2fg+ReuOSfwDX7rxnxLtrBRoI+3C6jbetSvSEHg6H3J3MAjo/ZaDTkZzpTpFRu/nx0FQJBtSroc22rgpqtULfPP7nvUfjqJXDz5+BJb4U3/XJW5VUjOwRr9/XX5lw5eE07DYFaDe3eOZXWPCAREZFFphBIRKQFu63EcTvYAz+BgRHYeN4CntXK9oonHcG7LjqKY9dElvpUajpVhzQTzxYdzTWqtTg53BBmh0CN25ZGwz4m7TX2J7wQXvcjSOyFL11kDTJvI18qUyhXiATmtoPNCYESe+HGf7MeY8OT5xyrm3Ywl8vgYy84kVefs8HR/SN+D26X0XYm0F27Ygx43T0HD/YMqKl08+djZuvb3K+x7Xynxqqg/z4fbvkCxHbNWQ8PVlBzqesWzrr6hTC5DV5+OTz3M+BtvfULqFWsRSOtX3t2lZCTFseE03YwuxKo7rnJFcvsT+QVAomIiCwyhUAiIi10NROomLXabI5/PriXR5XKoSga9vP+S47F414+//uKhv0zAYtDTtuihppcPLezazKDz+1iTcMK8DkbnzadD2/7tRVaXv4CuPUrLY+ZzpcBCPlmVy01DYF+8wkoF+Hij845TqViEssUuhrq/donb+TEdUOO7msYBoMBT9tKoD9sneCcLavweXp7/URD1rm3GpzcrhKoY2ufXRX02h+Aywu//CD828n8d/o9vDz9LWv1u2lCMcu6P/wtn/f9B1PBTfBnN1ihmwN2sDMaat8OBg4rgbrYcmfdf+a52d0isBQREZGF1XnnqojIChXPFon4Pc4Ch62/hkIKTnzRwp+YLCujIT+P7Et29TmxbNFRS9uwgzk39XZNZVi/agBXwwr20bCPWKZIsVzBa7+eo0dbQdAP3go/fy/suxee82nwzA5pUtXhz+HA7PMN+xtmAu29G+66As79C1i1Zc65pQolKmaXg9a7NBz0tRykvS+eY9t4mlc+yVllUTO1bXAtZkB1agezW/sGfG3aAI+62PpnchvTd/yI7A3f4Sm7/we+8CUY2QwuD97JR/mfyvMZP+pv+NuRjY7PfyKVZzjobRuC1SqBHMy5imWKuF3GnHlRjezZV/G6od121Vpjq5uIiIgsrOXzq1QRkWUmnnF2oQ7AAz+GgVWw6cIFPSdZfqx2sILj4c1gtdE4mTU1FOyuEmjnZGZmPXz9OYZbtDEFhuBVV8L574Xb/9eqCkqNz7pLMm89dtg/O7gI+T21gIip7fDtV1lryS98f9Nz63rQeg+GBrwtt4PduHUCgPOOmrs63alOVTKJbBGfp/msJyeDq2cZPZJ7N76BlxU+yp0vuwWe/+9WcOdyw2u+z9dDb2Y862xWlG0ile84D2lVyIdhzMwPaidefR0bHQbhB7xuBrzuWV+7vR6+2etVREREFo5CIBGRFmJOh9jOagVTgeVKEw35KZQrJB1sU7LFMs5m49RWazuYCWSaprVtqU0I1HTOi8sNF/8DvOQrsOcu+NLTrH9X2e1gYf/s840EPKQKJcypx+Brz4dixpo1NDDS9Py6HrTeg6bDl6tu3DrBaMjHcWt7nyc1aytXE+0GX3cdAjFTLXPY+o1w5hvhNd+Dv/gjHP3MuS1+DlghUPt2PK/bxUjQ17LlrZ7Tijaw5gbVh5m7pjKEfG5WhZy3B4qIiMj8KQQSEWkhlik4G2K79bpqK9hlC35OsvzYQ3adVE7YYtlCVzOBnAQHsUyRZL7EhtHQ3HO0w4t2s4tOfim8+ZfWn796Cdz8eaiUSVUrgUJNKoEOZxzza8+zXv+vvwrWntz2/GAm2FoIrYYvm6bJH7ZO8JSjonNa5boR8XvweVwtw5d2s556DYF8nrkznsB6TicctGzVG092rgSqHdtJJZDD2VZQrdJqmAm0YTTUsYpIRERE+kshkIhIC043OHH/j9UKtoLZF9VOh0PnimVyxYqjMCTkc+PpsPHKtrPFevj6c+x4Yb/uNHj772DzhfCrD8GXL8Z14AGAOdvB1lQOcKXvHyGfgjdcBYed0vbQ7ebl9MvwgLfp92rrgRQHknnOP2p0Xsc3DINoqHWVTLxNm5/9XtJVCDSZ4YiRuTOeoMmwbwcmUoXazJ92nB673dfbqLFKa+dUhg2rWq+zFxERkYWhEEhEpIV4tlgbaNqS3Qp2wgvUCrZC2ZuWnFYCJboIQwzDsNpoHAQHu9qEQLU2prSDcwyPwau/a7WHxXZx4W9fyvs83yXkrmt3i+3mWbe+hQgZnnjhlXDYqR0Pa2+ScroivhdDA14SuSKVyuz5TH/owzwgWzTin1c7WKuZRc3satHeB9ZzOpUuzPlaW8kVy6TyJUeVQGMRv6Mqo1i24Liyazjorc2FqlRaty6KiIjIwlIIJCLShGmazua2bL0Oimk44bJFOS9ZfmrtYA6rMmJdVsTYq8U72TWZBpqHQGG/B7/H5bx9yDCs9rB33crWNc/hLz0/Zs0Vz4Adf4D44/C15+IrJnlN4W+ZHjze0SHtCp2FrAQaCvowTUjmZs9nunHrBBtHg6wfmX/oMBrytXyu+zkTyDRNdk1l2NikvQ+sap1yxXQUEMLMPKgxR+1g/ubzoxq0a39rZLWDWa+/A8k8+VKlaeuiiIiILCyFQCIiTWQKZUoVs3Orw/0/guAobLpgcU5Mlp1Vweo2JYcBS21AstOBum2GHdfbNZVhLOJvun7cMAyrxaeLuUUABFfxsyM/wmsLH8KolOBrz4X/vgCyMR68+OvcZ26Z2RDWQSJbxN9ic1a/2D+vsbpB2qVyhVu2T/WlCgisgKRVJVCiTfVgJODBMGYqwTqZzhRJ5UstV6h32lTWyG5hs0PLdqJhP9limXSbYeflikkyV3IcAg0HfbXXcbuqNREREVlYCoFERJpwVK1RzMLDv9RWsBXOU92m5LgSyB6Q7GTeFNbFc8zBdrBdU83Xw9uiYR8TDucW1UvnS9zpOQ3jnTfDU/4SAoPwuh/hWn8mACmHW9GcbkSbD/v49XOB7n48Tipf4vx+hUARP5PpPKY5uw2rVN0Q1+o9w+UyiPg9jiuBdlYru1o9p3aLn9PX3UStEmjukOlGUQfHTnQZZg4NeMkVK+SK5Y5fm4iIiCwchUAiIk3YczvaXuA8eq3VCnbiixbprGS5Gg35WlaHNLJfW1210ThqB2s/Y6WnSiAglSsR8nvAF4JnfQL+6m5Yf6b1MboIgbIFx8FXr5q1XN24dQLDgHO3zG8otG005KNYNklkZ3/diWpFVLv3jPpqmE5q1TKjzZ/TsVolkLPXnX0/J5VA9vDodi1h3bY11j83u6cyuAxYN6zB0CIiIotNIZCISBMzm4zaXDDZrWAbz1+ks5LlqptNTbXXVhcVFJ2Cg3ypzN5ErmVgAFbliKPB0A1ShRLhwNxKt3A1BGrXMlSvm/kxvapVAtV9v/6wdYKT1g0xEupPAFULSBqebyfbzxrXpLezuxoCHdFijtGovZXOaSVQ9X72IPN2nLSadd3WGJwJgXZNZThsaACfR38NFRERWWz6v6+ISBPxTkNsCxl45FdwvLaCidUi1E0I5DIg4nf2uhkOeknmSpTKlZb3eXw6i2m2n7Fiz7Jxuk3KlsqVaoFPPftjSYchUDxbdBx89coObePVaqt0vsSdu6b7Ng8I6rbB9RgCOW8Hy7C6xYwnsOYfuV2G85lAyTxDA15HwctM0NW6ymimos1hW2P1frFMkZ1TGTa2CSxFRERk4SgEEhFpItbpt9xb1QomM7prB7OGB7tchqP726FCos0AZieDdkfDfkoV03EIYUvnm4dAAa8Lt8twPBg6ni12HrQ+T43tYH/aMUWxbHLeUf1pBYOZdqrG57vfIVC79fBgzRhaFfIxkXTaDpavhTudrApVh523aQdz8vXWs+8XyxS0Hl5ERGQJKQQSEWmiY6vD/T+CYBQ2nreIZyXL1VjETzJfIlcsd7xvt2FIfRtNK7s7zI+BmWG/3baEpVqEQIZhEPZ7llU7mM/jIuhz12Yo3fjoBD6PiydtWtW3x2jVKuUkFBkc8DreDrZrKtP2+bTPxenzOZHK114DnXirw84bW97q9doOtieWZSJV6Pi1iYiIyMJQCCQi0kQsU8TrNhhots7abgU7Qa1gYhkN2QFL56qMWLbIUND5fJqZNprWx945mWHA664NC27Gvm3cYeWIrVUIBFZLmJN2sHypTLZYXvDtYGC1SdmVfH/YOsFZG0f6upZ+JOjDZcydxRN3MPDbrgRq3CzWKFcssy+R61gtEw372rZs1ZtIFWoBlhPRsK9tJVCsU8tsA7sV8N4nEoDWw4uIiCwVhUAiIk3EswWGBnwYRpOWnUevgWIGTrhs0c9LlqdadYiD7VvxTKGripjBgbnDjhvZrUNNX69VtUHCvVQCNRkMDTiuBJoZhr2w28HA+n7Fs0UmUnke2pfs6zwgAHe1DasxfLG/xsE2z+1w0EuxbJLtUDH2RMya8dRpbo4158n5TKDuQqD2c65imSIhnxuv29lfJcM+Dy4D7n0iBsDGVSHH5yIiIiL9oxBIRKSJWKbYvhUsNKZWMKmJRjpvU7L12g7Wro1o12SGIxxUjYCzoMpmmibpfKm2Dr5RyO92tCLeHrS+0DOBwPp+xTNFbto2CcD5fQ6BwBoO3awdLOB1ta06mpmL074lbNdk5xlPUK3WSeUdVRal8iXHM4HAanFst34+li0w3EWo53IZDA142XogBagSSEREZKkoBBIRaSKebTG/pJC2KoG0FUzq1NrBHLTmxLJtAsYmhjsEB6ZpdhwiDDBstzE5aFmz5UsVimWzdTtYwEsq72wOEjhvHZqP4QEfsWyBGx+dYDDg4aTDh/r+GNGIb247WKv3jDqNg6tbsQd9dwr2RsN+csUKmUL752C8Gvy1axdsFA37a5/XTMLB19toOOijYsJgwLPgm+JERESkOYVAIiJNxDItqjUevdZqBTvxskU/J1m+7DabdoN0ASrV7Vw9tYO1CIEmUgWyxXLH1iGrjcn5KnugVuXTeiaQm1Su86Bj+9wXYybQ0ICXWKbIH7ZOcO6Ro7gdbmHrhtUqNbcdrF8hkJMZT/Z5QOcKNPt2e7OZE9Gwn2yx3LLdr5dB3/ZreeOoWsFERESWikIgEZEm4tli899UP/ATbQWTOQZ8bkI+d8dKoGS+hGl2VxHjdbsI+z3Ess2PvWsqDThrr4mGfV0Nhk53DIE8pB1UAtnzjOwh1wtpOOjlQDLPE7HsgrSCgdUOttCVQJ1mPEFdi1+HEMiu6Ol2MHS7Y3db0QYzVW1qBRMREVk6CoFERJpoekFXzFmtYMc9F1z92zYkh4ZopHOVTW02TpcDku2tUs3scrAevnaOXawUB0jmrBCo9Uwgj7OZQIvYDlYf3vZ7KLQtGvGRLpTJ1rVhWZUx7Z9XpyHQbgfr4aG+Eqh9sGff3u1MIOtzm79e4r2EQNX7az28iIjI0lEIJCLSoFiukMqX5lYtbPsNFFLWaniRBp22KQG1ap5uw5ChAW8tQGq0czKDYcD6kQEH5+jrqh3MrgSKtNgOFvF7SBdKVCrtBxPHMwUMo/Vx+sn+3q4bCrA5ujBtR9HQ3IDEyYycIQdDvp3OeILu28FGQ93NBAKazgUyTZN4pth2E1ozQ6oEEhERWXIKgUREGti/pZ/zW+4Hr4LAEGy6cAnOSpa70ZCvYztYy9dWB8NBb8sV8bumMhw2GMDv6VydNhr2OxpebbOrfNpVApkmZDqsPI9VAxLXAsznaWSHt+cdFe3YTtUre7ZOffjipB3MXpPebjvYeCrvaMYTwCqHA8nHk3mGBrz4PM7/2mdXAo03OXa2WKZQrnTd3qd2MBERkaWn1TYickh7xzdu48yNI7z9wiMdf459gTbrgq5UgId/AcdeCp6Fn2siB59oxM/tO6fb3mequpmr21XpIyEfP79nL1s+9PM5t1VMOGfzKmfnGPaTKZTJFEoEfZ3/CtBxMHS1siedL7W8D/Q2RLhXIyHrcRaqFQzmtmEVyxXShXLHr9HlMhhs09oHsGPC2WYwAJ/HxdCAt2Ml0P5Erjbjx6lVIWub3Id/fB//8JP7Zt1m132NdBlmjlRDKycBl4iIiCwMhUAicki7ceskmUK5qxBobzwLwNqhwMwHd1wPubi1Gl6kiWjIx1SmQLlittxIdeeuGANed9fbkf7iaUexpU1r09OPW+3oOKPhmcqR4Ko+hEDVjydzJdYMtj7O3niWtYOB1nfoo7M3reKfXnQyl5582II9xmg1BLKHQydqM486f0/bzXcCuHXHFAAnO1xtP9qhxc80Te7YFeP8o0YdHc/mdbv47MtPY9t4quntPreLZ5+0tqtjvuj0w1kV8rF+RCGQiIjIUlEIJCKHrHypTCpf4onpbFef93j1/rNmrDxwFfjCcOTT+3mKcgiJRvyYplXt02oA703bJjhr00hXbTkAJ6wb5IR1bVIWh8bqVtk7qTSpbQdrMcvHDoFarRG3PT6d5SlHLlxlTj2P28Wrz9mwoI8xGprdDma36jXdKNigUwh0y/ZJjl0TcbzJq9m6+nrbxlNMpPKce2R3IRDAZacf3vXntDMc9PHC0/p7TBEREemOZgKJyCHLbr15PJbtOLi23u6pDG6XMVO5UCnDQz+HYy4B7+JUM8jBx75ob7V9azyZ55H9qUULQ5qptTE1GfbbTKq6HSzobT5vyJ4V1G5DWL5UZl8i52hw9cEi4HUT8Xtq4Utt1pODGTntQqB8qcytO6a6CmzGOgwkv2nbJMCSvu5ERERk+VAIJCKHLHtYaqFUYbyLjUiPT2dZNxzA466+Re68CTITagWTtmrVIcnmVRm3bLcvxruvyOiXWjtY2tlw6FS+TNjvaTnQub4drJW9sRym6WzGzcEkGpkJX+xQx8m2rHYh0F27YuSKla5eI6Ph9gPJb9o6yfqRgUPu+y8iIiK9UQgkIoes+gvdx6czjj/v8ekM64frLpgevAo8A3D0M/t5enKIiUbar+u+adskkYCHE/vQ1tUrOwRyXAmULxLyt9465qQdrGl75SEgWjeLZ2Ym0PxCoJu2TeIy4JwtzkOgaNhPPFukUKrMua1SMbnlsUnO7eJ4IiIicmhTCCQih6ypuracx7uYC/T4dHbmgrVSgQd/Ckc9A3zdDfOVlSUaah8C3bxtgnM2r5qpMFsCfo+bSMDjuBIoXa0EasWeFdSuHcwOYA+1EGg05K9V4MR7CIFMc26L6s3bJzlx3VBXm9TsYG+qyXP64L4EsUyRp3Q5FFpEREQOXQqBROSQVd8isXvKWSVQrljmQLJuaO4Tt0FyL5zwwoU4RTmEDA548LqNpkN698Sy7JjMcO4ymMsyFvY7bo9Mdlj9HnYwE2j3dAZP/YytQ0Q0MlMJFM90FwKVKybpQnnWx7OFMnfumu66XXBmXf3c5/Tm6jygc7cs/etORERElgeFQCJyyJpKF/C4DEZDPseVQE/EGlpXHvgJuLzWUGiRNgzDqFaHtL4YX8p5QLZo2O+4HSydL7XcDAbg97jwuIwOlUBZDqufsXWIGA35mc4UKZUrxLJFgj63o61vw9UNYo0tYbftnKJYNrve4hWt2/jW6KZtk2yJhlg7dGgFcCIiItK7Q+tvZCIidabSBUZCPtavCjoOgWbmlwTBNK3V8EdeBIGhhTxVOUTUV4fUu2nbJKtCPo5dE1mCs5ptNOxzPhg6174SyDAMwgFPx5lAs2ZsHSLsGVBT6QLxbNFxC5d9P7t6yHbTtkk8LoMnbVrV3XnYw74bKtCK5Qp/3D7Z02p4EREROXQpBBKRQ9ZkusBoyMcRIwPsdjgY2m4bO2LVAOy9C+K7tBVMHIuG/XPawUzT5OZtEzx5y6qWW7YWU7TDSvF6qXyptga+lZDPU1sl38zuqYz183SIGauGL+OpfFchkL1BLJad/Tq5edskpx4x3PH73ahVO9i9T8RJF8paDS8iIiKzKAQSkUPWZCrPaNjH+pEge2JZypW5g1gbPT6dxes2WB0JWFVAhhuOe+4inK0cCpq1g+2czLAnnlsW84DAqgSKZYoUy3O3STVK5UtEOoQSkYCnZTuYPWNr/cihVwk0Wg1fJlNWJZCT9fAwUwmUqGsHS+SK3PN4rKd2waDPTcDrmvO6s1sQn7ylu8oiERERObQpBBKRQ9ZUusCqkJ/1IwMUyyYHkrmOn/P4dIZ1wwO4DazV8JsvgKAuosQZqx2sMGvz083bl888IJipHGm2TaqeaZqknVQC+VuHQHsaZ2wdQuorcBK9tIPVhUC3PjZFxaSn1i3DMJpWoN28bZLj1kZqYZWIiIgIKAQSkUNYrR2suulr91TnuUC7p7McMRKEAw/C5Fa1gklXoiE/hXKFZF0octO2SVZH/GyJhpbwzGbYM2Q6tYTlSxVKFbPtYGiwNoS1mgm0uzpjq7Zt7xAyWjeLp6eZQHUh0E3bJvF5XJyxYaTHc5nd4pcvlbl1x5TmAYmIiMgcCoFE5JBUKFVI5kqsCvlqVQiPO5gL9MR0xrr/g1cBBhz3vAU+UzmURCPVgKW6fcueB/SUI0cxjKWfBwT1FSztK4GS1Tk/7QZD27cnW4RA9s/coVgJFPF78HlcTKTyxDJFhh2GQGG/B7fLmBUC3bxtkjM3jBDwuns6l7Gwb9bzeeeuGPlSRfOAREREZA6FQCJySJrOWBdEq0I+Dh+2Q6D2lUDZQpmJVMG6YH3gKthwLkTWLPi5yqGjMWB59ECKiVRhWV2M186xw5p4u7rHSQjUqhJo1oytQ4xhGIyF/eyN58gWy44rgQzDYGjAWwuBptMFHtibmFe7YOOw75u3TeIy4OzNamUVERGR2RQCicghyV6XPBryEfC6WR3x1zZ/tfJEzLr9eO8+OHA/nKBWMOnOaMgeFmxdkNvDeZdTW06tjSndPgSy5/w4mgnUYjvY7qnqjK1lsBVtIYyGfWyfSAEwFHQWAoHVEharroi/xZ4ZdVTvr5HRsI+pdIFKdfj9zdsmOenwIcfBlIiIiKwcCoFE5JBkX+DaQ1HXjwx0rASyZwaduO/H4PLASS9Z0HOUQ0+tHawaAt20bYL1IwPLaiZO2O/B73F1bAezQ6BO28HCAQ/pQrkWQNR73J6xdYiKhv1sO5AG6CpwGayrBLpp2yRBn5tT1g/P6zzKFZNYtkimUOLO3dPLKngUERGR5UMhkIgckuzNR6tC1kX5+pEgj8faVwI9Pp3BT4HV235orYUPr17w85RDy6qgD8Ow2sEqFZNbtk8tm61gtpltUh0qgXLOKoHCfmuOTbowtxro8ensITkPyDYa8pEtlgEcr4gHKzCyV8TfvH2SJ21ahdfd+1/JZtbV57ltxzTFsrmsWhBFRERk+VAIJCKHpPp2MIAjVg2wJ5ajVK60/JzHp7Nc6r0dV24KznzjYpymHGI8bhcjQR8TqTwP7E0QzxaX5cV4tGGQcDN2qNN5O5gVfqTz5Vkft2Zs5Q/pECgamVm/7nQwtH3feLbIgUSOrQdS8w4K7Y1v46k8N2+fxOMyOGtjb5vGROT/t3ffYY6d9fn/70f1SBpp2u7OzBb3brCNvWBjcAADwY4BE6rpoQRCIIWQX0I6KaQRAiEEErpDTPtiMAYMCQaMabaxDcbdGLctMzu7U9RGZSSd3x9HR9NUR5qRdvR+XZcv74yko2e9Gtm6/SkAsLURAgHYkmbTeXk9ptKisXs4rGLJ1lQiW/Mx++YW9JrAd6Xh46Tjn74p58TW4wQsuZ6cB+TaNhBsOBi62e1gkXIlUCq3uOL77oytXmqF6zR3yLbUWjuYOxj6x+48oDaDwu3LBpL/6JczOmfPUMMKLgAA0J8IgQBsSTPpvIbDfnnKA2mX1sTXngtUOvKgnlC8Szr3tZKHt0esz2gkqJlUXj/65RGdsD2isVjvbcYaHQg0HAzd7HawaLlSKLlqOPS+8s/alq4EKlfgSOsLgX744BHFLJ/O2Blr6xxuO9gjR9K6c/98z7UgAgCA3sGnHABb0mw6V5kHJKkynLbehrAL57+qorzSOa/c8PNh69oWDWoqkdUtD/fePCDXtgEnqKo2zNmVyhVkjBQOeOteKxJwQqDV7WD7yz9ru7f4YGhXqzOBSrb0nfumdf4Jo21vTxsK+eX1GF1356RKtnRBj77uAABA9xECAdiSZtP5yrpuSZoYsmRM7UqgdDqt55a+q0e2PV2Kjm3SKbEVjUYC2j+XUTpf7Ml5QJJTOVIo2UpkF2veJ5UraCDgkzH1Awp3ZtDqdrD9cxkFfJ5Kq9JWNFquBIoEvC0Ndnarho6k8h0JCj0eo5FIQPdNJRXweXTuMcwDAgAA1RECAdiSZlJ5jSxr1Qj6vBqLWjVDoPhPv6wRk9L0KS/frCNii9q+bFjwBSf0ZkWG28ZUb0NYKltoaq6M2y6WWl0JNJfR7qFQpSVzK3IrgVppBZNWVg11Kih0z7L32GFZ/vrVWwAAoH8RAgHYkmbS+cpmMNeekZD2zVVvB7N+/mk9Vtou69SLN+N42MLc191p49EVLYm9ZPkg4VrS+ULDzWDSshAou7oSaEG7tvA8IEkaDgfkMdJguLU/56GwEwKNRgI6ZWygI2dxg70n92jwCAAAegMhEIBNZdu27p9KbuhzLBZLimcW13wA3z0c1oFqlUBHHtTI9E36XPFi7R7pzAcy9C+3IqNXW8GkpUHC9SqBkk1WArn3SedXVgLtm8ts6c1gkuQtt2ENhlrbxOVWDl1w4mjDdrtmVV53JxECAQCA2giBAGyqH/9yRs95/4164NDGBUFzC051w+pKoN3DIU3GM1osllY+4PZPqWi8utbzjBXbfoD1OG5bRJJ08Wk7unyS2sZiTmBQb1teOldQtIkQKOjzyO81K7aDpXMFzabzW3ozmOuE7QM6psWwayxmKeD16OJTO/caOX5bRKORgM7aPdSxawIAgK2ntf91BQBtcj90HpjP6JSx6IY8x2zaCYFGIisH0u4ZDqtkS5PzWR0zWv7QVshJP/uM7ghfKMu7s2P/Vx7966QdA7rlT5+pHT24Gt41FA7ohG0R/eThWf3W006sep9UrqAd0ca/B2OMBoK+ykp5yfn5lrb2ZjDXx1+7Vz5Pa/9PbSQS0A/e+YyODs3+raedqFdfcGxLA6oBAED/4b8UAGyqmXJAM1tnFkm73GuPDqytBJKcWSUV935VWpjRNZ5n9UXVAjZHLwdArvNPGNUtD8+qWGNNfDpXbKodTHJawlLLQqB9lfXwW/9nKmr5FQq0Poh5R9TqaOgc8Hk03KMzqAAAQO8gBAKwqdxWLffvG+FIulY7mFOVsKIF5rZPSUPH6NrkaX3xgRVwXXDCiJK5gu6dTFS9PZldVLSJwdCSMxx6eQjk/ozt6YNKIAAAgKMJIRCATeW2arl/35DnKA+7XT0YemLIksdoaUPYkQelR76v7Fmv1ny2yAdW9JXzj3cGCN/00Mya22zbVipXUCTYXIXLQNCnVHZ5CLSgoM/DjC0AAIAeQwgEYFNtSgiUzssYZ+7Jcn6vRxODoaVKoNuvlIxX+475dUn9Mb8EcI0PWjp+W0Q3PTS75rbsYkklWxoI+pu61oDlUzq/vB0so93DIWZsAQAA9BhCIACbajNCoJl0XsPhgLyetR9Adw2HnJlAhZz0s6ukUy/VwzlnQDXtYOg35x8/olsenlkzFyiZW5QkDTRZCRRZXQk0v7Dl18MDAAAcjQiBAGyqzaoEWt0K5tozHNa+2UxlILTOe93S/BI+tKLPXHDCqBLZgu6bWjkXKJ0rSnIqfJoRrTITiFAVAACg9xACAdhUc24ItIGDoWfS+TVDoV27h0M6lMyq9JOPS8PHSSderP1zGYUDXg2Hm2t9AbaK808YkaQ1LWFuVU8k0Pp2sGR2UfMLi7RXAgAA9CBCIACbJlcoKln+oLih7WCp3Jr18K7dwyGdqP3yPPYj6bzXSR6P9s0tML8EfWliMKRjR8NrhkO7gU6zlUADQZ8W8kUVSzabwQAAAHoYIRCATTO/4MwZ2R4NKp5ZVKFY2pDnqdsONhLWK73fVsnjl57wKklO6wofWNGvnLlAsyotmwtUCYGCzYdAkpTOFyohEO1gAAAAvYcQCMCmcat/Tto+INuW4pnFjj9HsWRrPrOokUiw6u17Bmy9yPt9PTb2LCmyTZKzzpoPrOhXF5wwqnhmUfdNJSvfS7caApUrhlLZgvbNLkgiBAIAAOhFhEAANk0lBNoxsOLrTppbyMu2VXMm0Pi+6xQzC/rRyOWSnCAqmS0wvwR96/wTRiVJNz+81BKWbDEEiriVQLlCZcZWrWo8AAAAdA8hEIBN44Y+J26PrPh6I56j1gdQ722f1MNmj25aPFWSKlULe0aoWkB/2jUU0p6R0Iq5QOkWZwJFyyFQMleoVNYxYwsAAKD3EAIB2DRLlUDRFV930kzKuWbVSqCDP5UO3q7vRZ+n/fPO3JKl+SVUAqF/XXD8qG5eNhcolS3IY6SQ39vU45dXAu2by/DzBAAA0KMIgQBsmtl0XsZIx7uVQBuwJn4mnZMkjQ5UmQl06yckf1i/3PncSvizf475JcD5J4xqfmFRD0w7c4FSuYIiQV/T1Txu21gq61QC7eHnCQAAoCcRAgHYNLPpvAZDfm0rr2+fTW1iO1g2Lt35RelxL9L27Ts0ncwpu1jU/rmMokGfBkP+jp8FOFqcf/yIJOmmXzotYalcoel5QNJSCHQwnmXGFgAAQA8jBAKwaWYXnNXtQZ9X0aBvYyqBysHScHhVqHPH56XFBemJb6hU/RyYz2j/3IJ2Mb8EfW7PSFi7h0O6+eFZSU5bV0shUHl20H2TCUlU1gEAAPQqQiAAm2YunddI2KnQGY4ENLdBg6GHwn75vMve3mzbaQXb+QRp5xMqVQr75zLaz/wSQJJ0fnkukG3blXawZkWCzuwgd808P1MAAAC9iRAIwKaZTecrbVojkYBmNigEWtMK9thN0uF7pb2vl7S0CWzf7IL2zS6wGQyQdMEJI5pN5/WL6ZRSuYKiTW4Gk6Sgz6uA16MHDjkhED9TAAAAvYkQCMCmWR0CzW3QYOg1m8Fu/YQUHJQe9yJJ0o6oJb/X6K4DcaXzRaoWAEkXnDAqSbrpoRmlsgVFAs2HQJJTDZQrlDTAjC0AAICeRQgEYFPYtq25hbyGl4VAGzEYeiaV12hk2Waw9BHpnmuks6+QAs5WMq/HaOdQSDc95AzBZX4J4Pwc7Cr/XKRzhcqcn2a599/NjC0AAICeRQgEYFMkcwUtFu1Klc5IJLAhg6Fn03mNDCyrBPrZVVIxL+193Yr77RkO65GZhcqvgX5njNH5x4/o5odmlcy2NhhakgaCTvUPlXUAAAC9ixAIwKZwh0APu4OhwwFlF0tayBc69hylklNtVGkHs23ptiulYy6Udpy+4r7Lq392UQkESHJawmbSeSVb3A4mSQPl4dBU1gEAAPQuQiAAm8IdAu1W6bhBzWwHh0PPZxZVsrU0GPrgT6XZX0rnvGLNfd0PqjGL+SWAy50LJKn1drDgUjsYAAAAehMhEIBN4VYCLV8R73x/sWPPMZvOOc/hhkB3XS15/NLpz11z3z0j4RV/B+Bs9ZoYtCSppRXxy+/PzxQAAEDvIgQCsCkqlUDLZgI538917jnKg6ZHI0GpVJLu/rJ08rOl0PCa+7rVClQtAEuMMZVqoGiLIVDUohIIAACg1xECAV3079/+he4+GN+U57r1kVl97PsPdex6tm3r/dc/oHsOJpq6/1yNEKiTa+LdoGl0ICDtu1lKHKishV/NHV7LEFtgpfOPH5G0jkqggBsC8TMFAADQq1r7LzwAHZPMLuq933pAqXxBZ+4c3PDn+8zNj+mrPz+o1z/leHk87a9vTmQKev/1v1AqW9AZO89oeP/ZdF4Bn0fhgDM8tlIJ1ME18ZUQKBKQbv+i5AtJp1xS9b7bB4J68Xm7denjxjv2/MBW8OwzxvTt+6Z19p7W3pcuPn2HFhaLzNgCAADoYYRAQJccnM9KklLZzm3HqufAfEaLRVuHUzmNxayOXE+SDsYzTd1/Nu1s7TLGCaBilk9ej+loJdBsOVAaDnmku6+RTr1ECg5Uva/HY/QvLzm7Y88NbBWjA0F99DV7W37chSdu04UnbtuAEwEAAKBTaAcDuuRgOURJ5TYnBHLDGje8aft68+71sk3df24hX1kPLzmzR4bDAc12eDB0zPLJ/9gPpIUjNVvBAAAAAKAfEQIBXeKGMclNqAQqlmxNxZ2w5mCnQqByqNTs9WbSeWdWzzKjkUBlo1cnOM8RdLaCBaLSSc/u2LUBAAAA4GhHCAR0SaUSaBNCoCOpnBaL9ornbZcbYh1O5pQrFBvefy69shJIkoYj/g6viM9rR1jSvV911sL72297AwAAAICtghAI6BI3jElkOxeC1LK8Bexgk+1bjSy/jltlVM9MOl8ZBu0ajQQ7viL+IvNzKRunFQwAAAAAViEEArqkMhh6E2YCuYFTwOvp6EyggNd5C2l0zcViSclsYU0INBzxa26hcyHYTDqvp+ZulELD0glP79h1AQAAAGArIAQCumQzZwK5IdDZewY7NxNoPlNZId2oumiuvLp9eFUINBIJam4hr2LJbvs8pZKtzEJSZyR+IJ1xueRlTTUAAAAALEcIBHRBsWRrKrFUCWTb7Ycg9Ryczyoa9OnU8WhHQqDFYkmHElmde+xw+fr1rzlbXgM/ujoECvtl21I80341UCK7qKfrdgVKGVrBAAAAAKAKQiCgC6aTWRVLtvaMhFQs2couljb0+Q7MZ7RzKKSdQyHNLSxqId9e9dGhRFYlWzphW0TbBoKNQyC3EmjNYOjAitvbMZPO63neHysT3C4d+5S2rwcAAAAAWw0hENAFbmhy6lhMkpTc4OHQB+cz2jlkaddQqPx1e8Oh3cfvHApp15DVcCaQG/JUGwy9/PZ2xOeO6Bmen2nmuMskj7ft6wEAAADAVkMIBHTBgXKIcur4gCQpucHDoQ8uqwRyv273epIq12wUAs3VCIGGI87cnk6EQL5ffENBs6j8aS9o+1oAAAAAsBURArVrg2e5YGs6MOeEJqeMRSVt7HDohXxBcwuLK0KgdjeEuY/fOehc8+B8pu5co5lyyDMUXjmsuZOVQKOPfE377W0KH39B29cCAAAAgK2IEKgdpZL0mZdJt3zU+TXQpIPzGcUsnyYGnVAmtYEhkFu1s3s4pLFoUF6PabsS6MB8RiORgEIBr3YOhZRdLNVd9T6Xzmsw5Jffu/Itx60EmltoMwRKz2j8yI/11eKTNTwQaHx/AAAAAOhDhEDtyCel0qJ03R9KVz5Pmn2o2yfCUcJtz4paPklSKrdxM4EOLJvf4/N6NB5rPMOnkYPzmcp8oV1NtJjNLiyuaQWTpKDPq4GgTzOpNkOge78ir13Ut70XKehjHhAAAAAAVEMI1A5rUHrVl6Tnf1CaulP60IXSTR+mKggNHSiHKANBJwRKbEIlkNsKtnPI6shMoJ1DlqSlEKhesDSbzml4VSuYazjib78S6M6rdSiwR4cjJ7d3HQAAAADYwgiB2mWMdO6rpbfeJB3/K9I33yl98lLpyIPdPhl62JpKoA0OgTxGGos683ecGT7r3w5m27YOzGVWhEru89Qym17USHn+z2oj4UB7M4HmH5Me/YFutC7WyED15wAAAAAAEAJ1Tmyn9IrPSy/4T+nwvdJ/PkX64QekUrHbJ0OPSWYXlcgWtGt4qRJoIwdDH5jPaDxmyVeex7NzKKTJeEal0vqGmieyBaXzxUoF0EgkoKDP0yAEymkkUr0SaCTSZgj0889Lkr5mX1QZNA0AAAAAWIsQqJOMkc55ufTWW6QTnyl96y+kH32g26dCj5mMr5zRE/J7N3QmkFt15No5FNJi0daRVG7d13OvI0nGGO2qU11k27bm6lQCDbcTAtm2dMfnpWOfonuzwxqtMncIAAAAAOAgBNoI0XHpiqukk58j/eB9Uma+2ydCD3Fn5+wqt1FFLZ9SuY1sB8uuCIHc513vcOjVIZD761rXS+UKyhdLNSuBRtsJgQ7cLs38QvZZL9PcQl4jbAYDAAAAgJoIgTaKMdLFfyZl49JNH+r2adBDVocoA5ZvwwZDl0q2JuNrK4Gcc6xvLtDS+a1l16w9bHou7VQ51asEyiwWlcmvo3Xyjs9K3qASJz5Xi0WbSiAAAAAAqIMQaCNNnC2dcbn04w9JC7PdPg16xMH5jLweox3RciVQ0Ldhg6GPpHJaLNqV6h9peQi0vkqgA/NZBbwebVsW6uwcCmk6mVOusDbImS1v/qo5EygcWHG/phXy0l1XS6ddptmCVX4OQiAAAAAAqIUQaKM9/U+lfEr64b91+yToEQfnsxqPWfJ6jCQpavmVzG7MTKADVVq3YpZf0aCvrXawiSFLnvL5l19/Kr62umg27cweGg5XD2jc4Gau1ZawB78lZWals6+oPAchEAAAAADURgi00XacJp31Uunm/5KSh7p9GvSAA/OZymYtSRoIbtxMILfla3kI5H693kqgg/MZ7RxceT3391MtWJott4PV2tzlBjctzwW643NSZLt04sU6knIeu40V8QAAAABQEyHQZnjaH0vFvDMkGn3vwFxmxTydqLVx7WAH5hckVQuBrHVXAh1YtW1s+fWrzRmqVALVWRHv3K+FECgzJz3wTelxL5a8/spjqQQCAAAAgNoIgTbD6InSOa+Qbv24FN/f7dOgi4olW1OJldu6BiyfkhsUAh2czyoa9GkwtDKAWW8l0GKxpEOJ7IoZQ5I0MWiVn696JVDA69FA0Ff1musKge7+shOsnn3FiscSAgEAAABAbYRAm+VpfyTZtnTjv3T7JOii6WRWxZK9IgSKBn1K5QsqleyOP1+1qh1J2jUc0tzCohbyrYVPhxJZlWzn8ctZfq+2DQRrhEA5DUf8MsasuU1yZhR5Paa1EOiOz0nbT3eGr0uaSeUVCXhl+b3NXwMAAAAA+gwh0GYZOkY67zekn35amn247l03IgxAb3BDkuUzgaKWX7YtpVsMZJp9vp2rqnaWP3+ra+JrzRhyrlm9xWw2vVhzKLQkeTxGw2F/w+1gtm3rSCqnmcfulfbdrNRpL9LhVF6HkzlNJTIaGaAKCAAAAADqIQTaTBe9Q/L4pO/9c827fPveQzrrr/9P8YWN2RaF7jpQDlGWV9IMWE6b1EYMhz5YoxJovWviD1bZNrb8mtWuN7eQ12iDgGY4HGi4Hey9//eA9v7d9fr0R/5ZJdvoWd8a0xPffb2e+O7rdd2dU9oRXRt2AQAAAACWVB/SgY0Rm5Ce+Ebppg9JT327tP2UNXe5/t5ppXIFHZjPaDBcfZAujl5uSOLO0JGcwdCSnOHQg517roV8QXMLix0NgSor5werX/OG+w/Ltu0VrV+z6bzO3Bmre92RSEAzDUKgGx6Y1mljA3pD/hYdCj9Jb33i01bcvvfY4WZ/GwAAAADQlwiBNttT3y7d+knphn+QXvLJNTff+sisJGm+QWsMjk4H5zOKWT5FraWAzx2YnOjwcGi3dWtXlRBoLBqUx6yvEmgkElAosHb2zs6hkDKLRc0vLGp42YDm2XReow0GNo9EAnpwOlXz9lSuoHsOJvTu89KK3rVf0ef8uV59zrEtnR0AAAAA+h3tYJstsk264C3S3V+Spu5acdP8Ql6/KH8Qns/QDrYVVWvPim5QO1i91i2f16PxmFVpT2vlmtVmDEmqbAxbPhdosVhSPLMyFKpmJBKoOxj6Z4/Nq2RLT8tcL/nD0unPa+ncAAAAAABCoO648G1SICrduHI20O2PzVV+PUcl0JZ0YD67pjLHrQpKZjsb/C2FQNVDm/WsiT84n63aCuZeb/nzStJ8ebZVM5VAcwv5mkPRb310VkGT18T+bzoBUHCgpXMDAAAAAAiBuiM0LJ3/Zumer0iH7ql8+9ZH5uT1OLNU5hkMvSVVqwRy28FSHW8Hy8hjpLFYnRAo3no7WLXKIvd67n1cbpjZTCVQyZbiNSrgbnt0Tq8evk8mF5fOvqKlMwMAAAAAHIRA3fLkt0qBAenG91S+deujczpzZ0whv7fhpiQcfVK5guKZtYOaN6od7MB8VmMxS35v9R/znUMhTc5na1bfrJbILiqZK1SdMSQ51T4Bn2dFO9hMynkdj9RZES85IZCkqmviiyVbP31sXi/1fleKTkjHP23NfQAAAAAAjRECdUt4RHrSm6S7vyxN36d8oaQ79s3rvGOHNRz2a45KoC1nskZ7ViSwUYOha1ftSM4Mn3yxpCPpXNPXk6rPGJIkY4x2DYUqA6mlpUqgkSZWxEuqGn7eN5XQcP6ATk7eIp37Wsmzdig1AAAAAKAxQqBuevLbnCG3N75Hdx+MK1coae+xIxoMB9gOtgXtL4coqytpPB6jgaCv4+1gBxqEQO5tB+aaawlz71drxpB724pKoHRrlUDV1sTf9uicXun9jmQ80nmvbeqsAAAAAIC1CIG6KTIqPemN0l1X68F7bpck7T3OqQRiO9jWU6+SJmr5OjoYulSyNRnP1GzdWn6Og01uCDtYI8Racc3BlcOm3cqeZmYCLb//cj97aEov831POvVSKbazqbMCAAAAANYiBOq2J/+O5A9pz10f0u7hkMZilobDAbaDbUEH5zPyeox2RINrbhsI+jo6E+hIKqfFol1Z217NruG1g5zrOTCfVcDr0baBtedffs3pZE65QlGSNJvOK2r5as4lctWrBBp85DoNKyHzxDc2dU4AAAAAQHWEQN02sF323tfrianv6Ncm0pKkobCf7WBb0MH5rMZjlnxVAhGnEqhzIdCBBvN7JClm+RUN+la0b9VzcD6jiSFLnvIGu2rc5zsUd+YMzabzlYCnHsvvVTiwdiD6ZDyjy3LfUDx8DAOhAQAAAKBNhEA9YP8Zv6m87dPLsl+Q5AzJnV/IN721CUeHA/O127MGLL+SHawEclu86oVA7u3NVgIdnM9o52D967m/PzdYmltoLgSSnGqg2VUh0P133KS9ngeUfvxrJA9vVwAAAADQDj5V9YBbpn26qvhMnTD5dWn2IQ2F/SrZ6mhlCLrP2dZVvT0rGvQp1cGZQI02ebl2Dlk6GG8hBGoiVFr+/DOpfMOh0K6RSGDNivjIz69U1vZr+0Wvb+oaAAAAAIDaCIF6wK2Pzukq3wskr1+68b0actdlMxdoyyiWbE3FszVDlI1oBxsI+hSzfHXvt3PVSvdaCsWSphLZujOGJGli0Ln94DoqgYbDgZXtYNmEzjzyTd0cfrr8A6NNXQMAAAAAUBshUA+47dFZHXPMCTLn/YZ0x2c1UZqSJDaEbSGHkzkVSnbNEKjTg6HdqiNjas/vkZwQaDadVyZfrHu/Q8mcSnbjyiLL79W2gYAOxjOybVszTc4EkqTRSGDFYOjc7Z9VWBntP+kVTT0eAAAAAFAfIVCXxRcW9cChlPYeOyw95fclj0+n/OKjkqgE2koONFivHrX8WsgXVSiWOvJ8B+ONW7eWn6dRS1iz7WXufQ7MZ7WQLypfKDVfCRRZVglk2yrc8jHdWTpOu858SlOPBwAAAADURwjUZbc/NidJOu+4YSk2IZ37Gm178Is60zyseUKgLaNRiDJQbttK5+pX5DT/fLVbz5ZbPcOn9vVaCIEGQzowt1AZ8jzcwmDodL6o7GJReuwmReYf0FXFZ+vc40aaejwAAAAAoD5CoC679dFZeT1G5+wZcr7x9HfKjuzQfwXep4W56a6eDZ2zFKLUGAxdDoGSufZbADP5ombT+ZpVR8u552kUAh1ocP6V13TmDLmtXa0MhpbKFXC3flxpE9G9235VMcvf1OMBAAAAAPURAnXZrY/M6cydMYUD5QG+kW2yX/o/2q55PfWOP5aKbAjbCg7OZxS1fIrWCDSiwXII1IHh0G5rVzOBzVjMksdIBxoMhz44n9Fw2L/0Oq1j55ClzGJRDx9JSZJGBloLgeJHDsq++xpdU7pIjztuoqnHAgAAAAAaIwTqosViSXfsn9d5xw6v+L53z3n6B/NGHRu/RfrO33bpdOikA/OZupU5bjtYJ4ZDH5grh0CDjSuB/F6PxmJW5TH1rtlMK5i0NGfozv0JSa1XAgXv/IxMaVGfyD9Te48bbvAoAAAAAECzCIG66O6DCWUXS9p77NqZJzdELtEPBp8n/fD90t3XbPrZ0FkHGszocSuEktn228Hc1q5dw82FNk77VqOZQM3NGHKvJ0l3HYxLar4SaDgckEcl7Xjgs5oa3qtf2ruq/mwAAAAAANaHEKiLbn1kVpKqVjsMhQP6ePTN0u4nStf8tjR932YfDx3krmyvZaCT7WDzGXmM0+rVjF1Doaa2gzUzY0haCp/uOZiQz2MqrW6NjEYCutjzU0UWDugb1mXaEQ1qd5NBFgAAAACgMUKgLrrt0TntHg5V/bA+HPbrcEbSS/9bCkSkz71CysY3/5BoWypXUDyzqF1D4Zr3iXWyHWw+q7GYJb+3uR/vnUMhTc5nVSrZVW9PZBeVzBWaDoFGIwEFfB6lcgUNRwIyxjT1uEFPVn/l/2/NWXv0qdkztfe44aYfCwAAAABojBCoS2zb1q2PzmnvsdVnngyHA5pLL0qxndJLr5TmH5W+9GapVNrkk6Jdk01s1nJnAnWqEqjZ1i1J2jVkKV8s6Ug6V/N6UnPr4SXJGFMJjEabXA8vSZ7r/0I7zYw+PPSHejRe0Hm0ggEAAABARxECdcm+2YwOJ3M677jqH3SHwgHNLzgrtnXshdKvvlt64BvSjf+8iadEJ7jr1etV0oT8Xnk9RqkObQdrJQRy73uwxoawRuvtq1/Tue9wk0Oh9eD10m2f0hcDl+vK/WOSVDMgBQAAAACsDyFQl9z6aHkeUI0PukNhv9L5ovKFcuXP+W+WzrpCuuEfpP/9M6lU3Kyjok1uuFIvmDHGaCDoa3swdKlka3I+22Jg44ZA1ecCuevjm20Hk5Y2kzU1FDozL33ld6Ttp+nakdcpVygp5PfqjJ2xpp8PAAAAANAYIVCX3PronKJBn04Zi1a9fTjsbIuaz5SrgYyRLv+g9KQ3ST/+oPTZK5gRdJQ4OJ+R12O0Ixqse7+BoE/JNmcCHUnnlC+WWgtsGoRAB+cz8nuNtg3UP3+1aza1Hv6bfyKlDkkv+LCikQFJ0tl7BpueaQQAAAAAaA6fsrrktkfm9IRjh+X1VB98O1T+8Dy/sKwyxOuXfu090nPfJ/3yO9LHni3N/HIzjos2HJzPaDxmydcg1IhavrbbwSpVR4PNh0Axy6eBoK/Strb2mhlNDIbkqfFarcYNoYYbzQS67zrpjs9IF/2BtOvcyv1ZDQ8AAAAAndfc7mZUZdu2rrtzSruHQzp7z1DTj4tnFvXAdFKXnTVR8z7uLJW5dH7tjXtfL42eLH3h1dLHnulsEDv+V1o9fk+ZimeVXSzquG2Rjl3ztkfndNbuzako2Te7UGnxW+2ug/Gm2rOilq/twdCtDnGWnFa0nUOWbn9sXl/+6f41t999MNFSe9ny5687GHphVvrq70ljj5d+5Y9W3H/vccwDAgAAAIBOIwRqgzFG77z653rRebtbCoHuPhiXbUvn1HnMULkdbG6hxoyY4y+SfvO7TlvYp39duvSfpCe+sYXT95a/uvYu7Z/L6Ou/e1FHrjcVz+pFH/6R3vuSs/Wi83Z35Jr1/NW1d+s7903XvP2V5x/T8BoDQZ+OpKqEfi1YzxBnSTp1PKav3nFQb//8fNXbn3LisS1d7+SxAQV8Hp28Y6D2na77QykzJ736S5LPCX9O2jGgqOXTuQyFBgAAAICOIwRq09igpal49a1KtUyWW3b2jIRr3sdti6lsCKtm5HjpDd+Srn6j9PV3SHOPSr/6ty2dpVc8OrNQsx1pPWbLFVSPzi507Jr17Jtd0EUnb9PfXv64qrfvHm5cmRO1/Hr4SLqtc0zGswr5vRoM+Vt63Htfcrbe8exTat7ezPmXG4tZ+tlfPlvhQI23mLuvke66WnrGn0vjj698+/Jzduo5Z44rFPC29HwAAAAAgMYIgdo0MWhpMtFaCDRVvv94rHa1xlDIHQzdYFuUFZNe/lnp638g/egD0knPkk54Wkvn6QWHElklswWlcwVFgu2/LNN5p63qUIsB3XpNJbK68MTRttrZBiyfUm0Ohp5KZDU+aMmY5uf3SFLA5+loK56k2gFQ8pDzep04R3rq21fcZIwhAAIAAACADcJg6DaNxyxNxVurYJmKZzUY8tf9sBsOeBXwejRXrxLI5fFKl/yjNHy89LXflxY7V1GzGbKLxUrb21SLgVotbpjSakC33udKZgsab2EYczWdmAk0Fc/WDRe7bjEjff6VUn5B+vX/lLzk0AAAAACwWQiB2jQ+aOlwMqdCsdT0YybjWU0M1v+gbozRUNiv+XSDSiCXPyQ97/3S7EPSjf/S9Fl6wfJ2ulZb62pJl0OgVgO69XDPPD7Y/Ar1aqJBn3KFkvKF5l9L1c7S6LXVNbYtfeVt0v6fSC/8iLTj9G6fCAAAAAD6CiFQm8YHLZVs6XAq1/RjDiWyGmuiWmM4HGiuEsh1wtOls18h/fD90qF7mn9cly2v/ulUCOSuWu/U9eo5VGnva68SaKDcBrfelrBSyXZeW70aAn3vn6W7vig986+kM57f7dMAAAAAQN8hBGqTW3Ux2ULY0EwlkORsCJuvtR2sll/9OykYk776u1Jp/RUlm2lFJVCH28ES5TlDG8n9s2+3AidqOXOgktkW/8zLjqRzKpTs3qwEuutq6Ya/d0LKVXOAAAAAAACbgxCoTW5FT7MDiPOFko6kchpv4oN6y5VAkhQZlS75B6fl5taPt/bYLnFDFMvv0WSH2rfSuWLl150KlmpxW86a+TOtZ8ByKoHWOxeo0pbWazOB9t8qXfPb0jEXOi2LLQ6tBgAAAAB0BiFQmybKw4CbrQSaTjb/QX0o7G+8Hayas17mtIZd/9dS4mDrj99khxJZRS2fjhuNaCrefFtdPe52MGnjN4RNJbIaCvtl+dvbahW12msHW5pN1EMh0Pw+6bMvl6Lj0sv+R/K1NzcJAAAAALB+hEBtGg77FfB5KnNhGmnlg/pQOKD5hbxs227tUMZIz32fVFqUrvv/WntsF0zGMxqPWRoftDSV6Ewl0PIgpZVWvfXo1EauaNBtB1tnCJTosRAol5Q+e4VUyEmv+IJTpQYAAAAA6BpCoDYZYzQes5oOGpbmxzQeIjwc9muxaCudLza87xojJ0hP+2Ppvq9J936t9cdvoql4VuODliYGrY5uBxuLOVUnG90ONlk+f7sGKpVA65sJNBnPyucx2hbpgWqbYkG6+jel6Xull3xS2n5qt08EAAAAAH2PEKgDxlsIL5Y2STU3E0iS5tItzgVyXfg70o4znWqgbGJ919gEUwlnUPZ4LKQjqXxbK9Jd6VxBo5GgBkP+Dd8QdijRmbXs0TZnAh2KO1vnPJ4uz9zJL0iff6X0wDekS/9JOumZ3T0PAAAAAEASIVBHTAxaTVebTMazCvm9ioV8De87FHbag1reEOby+qXnf0BKTkrXvk1a7EyrVSctFkuaTubK7WBOBUuzrXX1JLMFDQR9mhhsvkprPXKFoo6k8m2vh5eWVsSvNwTqVEVSWxZmpf++XHrgf6XL/lV60m929zwAAAAAgApCoA4YjzmVQM3M7pkqr4c3TWxIGnIrgVrdELbc7r3Ss94l3fMV6ROXSPOPrf9aG+BwMifblsYHQxovt8h1on0rnS8oEvR2dM5QNdMJZ5C1G2C1w/J7FfB62poJ1NUQaH6f9InnSJN3SC/9b+mJb+jeWQAAAAAAazQuR0FD44OW8sWSZtN5jQ7UDwOmEtnKWvlGht1KoPVsCFvuqb8vbTtF+vKbpY88XXrxJ6UTntbeNTvEDXwmyjOBJHWkfSudK+q4UZ8Ggj7ddWDjWuGWhjG3XwkkOXOBUrlFybalmQed4cp2SSoVpFLR+btdlDx+ac/5ks8JCm3b1lQ8q4tP29GRc7Ts0N3S/7zIaQV79Zel457SnXMAAAAAAGoiBOqASniRyDYOgeJZnX/8SFPXdSuB5tupBHKd9mvSb35H+twrpU+/QHrWXzszg5qoSNpIbuAzFrMq4VgnQqBUzmkHGx+0dCSVU75QUsDX+cK3pUHfHajAyczp+b6bdOnDd0vv/amUmqp///A26ZxXSOe+VonwscosFuufw7adIMnb4R/7R37orIEPhKXXf0MaO7Oz1wcAAAAAdAQhUAcsDy/O3DlY836lkq1DLbTsuDOB5tJtVgK5tp0s/ea3pWt+W/rWX0gHfypd/kEpEOnM9ddhalmIErN8Cge8HZnhk84tzQSSnDlDe0bCbV93tUPLQqx1OXy/dM+10oPXS/tv0bvsktKLA9Lpz5ZOvFiKbJc8Psl4nL97fJLHKy3MSD/7jPTj/5B+9AH5Jy7Q5Z5ztXPgjKVrL8xKB26XDtwq7b9VOnCbU1k0/jhp13lLf42eLHnWEZDZtnT3l6Qvv0UaOkZ69ZecvwMAAAAAehIhUAe4694bhRdH0jkVSnbTIZDf61E06GtvJtBqwagzr+UH75O+87fS4fukl1wpbT+lc8/RgqlEVkGfR0Nhv4wxGh+02h4MXSrZWsgXFQn6KuHMRoVAk/GswgGvYtY6fpTu/4b0+Vc5LV4T50gXvUN/cfeEfuE/VZ97yVMbP/60y6TkIelnV8nc/An9W+AmFb5xlXT/Rc6f68yD5Tsaaftpzv1Dw074d8fnpZ98zLk5GJN2niPtfqJ03FOdNrNawaBtOzN/7rpauvvLUnyf87hXfEEKN1fhBgAAAADoDkKgDtgeDcrrMQ3DC7fqpZn18K6hiL8z7WDLGSNd9AfSxNnS1W+QPnyh9JTflS76Q6elZxO5G63cQdnjMUuT8fYGOafzzmBlpxKouYBuvaYSmRXnb9ovrpe+8Bpp/Czp5Z+VouOSpMl9tyo+38LvPzomXfQH+krghbr2K5/Xx065W76pn0pjj3daxXbtlXY+QbJiKx9XKkkzv3Cqgw7c5lQK/eD90vff61Qb7TzXmetz7FOlY86X4gec4Oeuq6XZXzr3OfFi6Rl/Jp3565K/y1vJAAAAAAANEQJ1gNdjtH0g2DBoWJof0/wQ4aFQoP3B0LWc9EzprbdI3/pL58P/z/+fdOk/OfODNsmheHZFKDY+aOmmX860dc10rihJipRnAkmdWTtfzdSq8zfloRukz73Cqc559Zec6pyyqDsYukWTibx+bD9Ovpf+f1Izs488Hmn7qc5f57zC+V4uKe272Znx88gPpB/9u1MxZjzOcGrjcSqFnvK70unPp/IHAAAAAI4yhEAdMj5oNRxo7AYRYy2sEx8K+zW3sEEhkCQN7JB+/T+lJ7xa+vo7pM+9XDrlUunSf5SGj9u45y2bTGR03jFLIcjEoKXpZE7Fki2vZ31Dq90QJRJ02rRC/s7MGapmKp7VBSeONv+AR34gfeYKafQk6TVfWREASeUQaB0r4qfiWY1Ggu0Nvw5GpZOe5fwlSfm0Ewo9+mNnNtEZlzuVRwAAAACAo1Ln1yX1qYlBq7IuvJbJeFY+j9G2SPMh0HA40Pl2sGqOe4r0W9+Xnv230sM3Sv9xvvS990iF3IY9pW3bOhTPaWzZjKTxmKVCydZMav3PmypXAg0EfTLGOH82GxACFUu2DiVzzW8Ge+xm6aqXOsOTX/OVqpU0A0GfktmCbNtu6SxTiWxnNpQtF4g4LV8X/5l0/psIgAAAAADgKEcI1CFjscZBw1Q8q7GYJU8LFS7DYb/m0psQAkmS1++0+rztJ9Ipl0jf/TvpI89wBgFvgNl0XvliSRMr2sHan+GTzjmVNJGgr3zNxgHdesyknIqlptrBDtwmXfViZ/bPa6+VBrZXvduA5VOhZCtXKLV0lql481vnAAAAAAD9iRCoQyYGLaVyBSWztVu31vNBfSgcUCJbUKHYWijQlsFd0kuvdDY+LcxIH71YuuEfpWJn29LcoGd82Ywkt5qlndAmlVsaDC051UUbUQlU7fzV73iH9Olfdyp/XvvVyhDoaqKWX5KUqPM6qn6WTOuziQAAAAAAfYUQqEOaGUA8lWg9BBoOO6FAfKOGQ9dzynOk3/6xdOYLpRv+wQmDDt3dsctXtqUt+2firnRvJ7RJrw6BymvnS6XWWqwaWRr0XefPdH6f9OkXOmvYX/tVJ2CrI1o+cytzgRbyBSWyBSqBAAAAAAB1EQJ1iFuFUauNybZtTcYzK1qfmjEUDkjSxm0IayQ8Ir3oo9LL/kdKHJT+62nOJrFi68OLV3OrfZaHKKORgPxe09F2sIlBZ87QkXRn5xtVBn3X+jPNp51B28VF6VVfcmYBNRC1yiFQrvl/vlPNhFEAAAAAgL7HdrAOmWgwyyaRKSi7WFpHO5hTCbQpw6HrOf150jFPlr7+B9K3/0a651qnUmjkRGfT1eiJUmiopUtOxbPyeoy2DSwNyvZ4jMZiVlsr3ZcPhpZWVhftiHYuKJmMZ+X3Go1GAmtvtG3pmrc4lVOv+IK0/ZSmrumeOdlCJVCloop2MAAAAABAHYRAHbIj5gQZh2qEQJOJjCStox3MCRjm0l2qBFousk16yZXS3V+Svvv30o3vkexls4rCo04gtOMM6eyXS3ueJJnaQ7An41ntiAbXrIIfj1majGfWfcxUblEeI1l+p9BteUB31u51X3aNqXim9qDvG98j3fMV6Vf/Tjr52U1fc8BaRwiUWNtWBwAAAADAaoRAHWL5vRqJBDRZo4KlqfkxVVRCoG5XArmMkR73IuevQk6ae1SaedD5a/aX0swvpTu/KN32SWns8dITXy89/qVScGDNpQ7VmJE0PmjprgPxdR8xnSsqUl4P717Pfb5Omkpkq1ff3PtV6bvvdoKwJ7+tpWvGyoOh6w0YX22yymwlAAAAAABWIwTqoHpbqNwKoZrzY2oYirjtYD1QCbSaL+i0Oa1udcqlpDv/n3Trx6WvvV36v7+Uzn6ZtPcN0tgZlbtNxjM6dTy65rITg5auv/eQbNuuBDmtSOUKlbYqqTNzhqqZimf1uF2Dq755l/SlN0u79krPfX/dSqhq3HO3OhMoZvkUDvDjDAAAAACojcHQHTQ+WDsEmoxnZYxankkTDfrk9ZjeqQRqRnBA2vs66c3fl97wLem0y6TbPy19+MnS/7xIWpiV5IQX1UKxsZil7GJp3RvR0rlCZSi05MwZ2hHt7Jp4Z9B3dmVlV/qI9NmXS1ZMuuIqyd96ZY7bDtbKdrCpRLbS8gYAAAAAQC2EQB00PmhV5rOsNhXPattAUAFfa//IjTEaCvm7tx2sHcY4c4Fe+F/SH9wrPetd0sM3Sp/8NaUOP6Z0vli1Pa7RkO1GVlcCOdfsbAgUzywqVyhp3A1fCnnpC6+R0tNOABQdX9d1/V6PLL9HyRYrgWgFAwAAAAA0QgjUQRMxS7PpvLKLxTW31Zwf04ShsL/728HaFRmVnvp26VVXS/H9Cl55iY43k0shyjJuoFErUGskXSUEGqsT0K3H5OqNXN/8Y+nRH0rP/6C067y2rj0Q9Lc0GHoyvv7XFgAAAACgfxACddBYObyYTuTW3NZOtcZwONAb28E64fhfkX7ja7IXM/pi4F06YfEXa+5SCYHWWbnjDIb2rvjeRHnjmG3b67rmalPLhzH/5GPSrZ9wQq6zXtL2tWOWr+nB0PlCSTPpHJVAAAAAAICGCIE6yG1tqrbefDKeaXkzmGsoHDi6ZgI1svMcXf/kT2vBtnTG/75ceuh7K27eEQ3KmPbawSKrKoHGB505Q4lM8xU29bhVRccmbpW+8cfSKZdIF/9FR649YPmaHgw9nczKtlvfOgcAAAAA6D+EQB00UaONaSFfUCJbaHkzmGs47O/N7WBteLA4phfl3yUNHSNd9WLp7msqt/m9Hm0fCFY2qrWq2kwgt1JmMrE2oFuPyXhWx5hDGr3uTdLIidILPyp5vI0f2ISo5Wt6MLRbkTRGCAQAAAAAaIAQqIPckGd1G5P79XqrNYYjW6wSSE6IUoyMyfP6b0g7nyD9v9+Qbv1k5fbxQUuT65jhY9v2mu1g0vIqrc7MBZqbndEngu+TsUvSyz/rbATrkIGgr+mZQG7gSCUQAAAAAKARQqAOilp+DQR9a4KGFfNj1mEw5FeuUKo6cPpodShRnpEUGpZefY108rOlr/2+9MMPSHIGLk9VaatrJFcoqVCyq1QCOQOo11tdtEKppBc88jc6Xvull14pjZ7Y/jWXGQj6m24HqwSMMVbEAwAAAADqIwTqsPFBS4dWVbC41Rrr3eA0HA5I0paqBlqx0SoQll52lXTmr0vf+gvpO+/WRCy4rsHQ6XJ4sjoEanfO0Ao3/L3Oy/xIXxj9bemEp7d/vVWilk+JJgdDT8azsvwexUK+xncGAAAAAPQ1QqAOG49Za4KGyTYrgYbDfknaOhvCJE3FMyv/efgC0os+Lj3hVdKN/6wXHvmQEtnFSqjTrHTOqZZa3Q7m93q0bWB9wdIKd10t3fgeXa2Ldd+eK9q7Vg3R8mDoZjaZTSWymhgMyRizIWcBAAAAAGwdhEAdNj5oVZ0JNBjyKxxYX7XGULkSaH6LVAJlF4uaW1hcO8fG45We9+/S+W/R2fs/o3/0fVRT8+mWrp2qVAKtHdI8MWitGdrdkgO3S9e8VcXd5+tPsq/V+FB4/deqI2r5ZNvSQr5x+9/U8ooqAAAAAADqIATqsIlBS4dTORWKpcr3phLtfVAfjpQrgbbIhjC3Xa7qtjSPR7rkH7TvrN/RFb4bNHjdW6Ri879vNwRaXQnkPt+6K4F++V3pyudLke3a9+z/Ul5+jQ8G13etBgaCzp93M8Ohp+LZdVeYAQAAAAD6CyFQh43FLBVLto6klqp22v2gvtVmAk1WtqXVGGZsjAq/8id69+IrtO3Rr0uff5W02NyQ6HSdEGhi0NLkOoZN647PO2vsh46RXv9NHViMSpLGN2gY84DlnD2Vqx9+lUr20oBtAAAAAAAaIATqsKVV5Ethw2Q829YK78GQUxkSz2ytSqB64cV4zNJHi8/VDSf/qfTA/0off7a0/7aG13YrgUYT90pffov00A1L1xy0lMgWtJBvcs6QbUvf/1fpy2+Sjnmy9PpvSIO7ljZybVD4Ei2HQIkGlUBH0jkVSjbr4QEAAAAATSEE6jA32HCDjnyhpJl0rnrrU5Msv1chv1dz6a1VCVQvBAoFvBoK+/XtyGXSFZ+R0kekjz1T+trbpcxczccV4wf1Ht9/6pgv/pp0x2ek/36B9K2/koqLlZa8plrCSkXpuj+Uvv3X0uNeLL3qaskadB7fRIjVjmi5iinVIARyfx/tvLYAAAAAAP2DEKjD3KDBDTqmk1nZdvtVI8Nh/5aZCTQVzyoa9K1Z475aZdPaab8mvfUW6YK3SLd9SvrgE6U7PudU6rjyaemGf9JlN1ym53t/pPyT3ia9437pvNdKP3y/9Inn6FjPocrz17WYkb7wGuknH5Mu/F3phR+VfEvzfybjGQ2F/bL8a4dPd0LUam4m0EZXJAEAAAAAthZCoA4biQQU8HoqH9Cn2lwP7xoKB7bMdrBmZySND1qaSpTb6qyYdMk/SG/6njR0rPTlN0ufeq40fa/0s89K/75XuuHv9fDIU/Ws/Hvkfc7fSNFx6Xn/Jr3kSmnmQT3h68/X5Z4f1N4QVshLj93kDIC+7+vSpf8s/erfOsOqV5w/t6EbuZqdCbTRFUkAAAAAgK1lfTvLUZMxphxelEOgDn1QH474t85g6CaHGU8MWrrrQGLVN8+S3vAt6fYrpevfJX3oAuf7O58gvfgT+uLdQzp86BH5vMuCmzNfIO06T/YX36h/2/8h3Xfzo9KZ/yUZr7T/J9KjP5Ie/aHz60JW8lnSS6+Uzri86rmmEpkNDV7cmUCNKoEm41n5PEbbIhuzpQwAAAAAsLUQAm2AShuTlrXstLlJaigc0OR8ovEdjwJT8YxO2bG94f3GYpaOpHLKF0oK+JaFOh6PtPd10mnPlW75L2nbKc7cHo9HqdvvrN5mNrRH3td9XR/627fotw59Ufq3s6VsQiotSsYjjT9e2vt66dgLpWOfIoVH6pw/q8fvGlzPb70pkUBzIdCheFZjMUsej9mwswAAAAAAtg5CoA0wPmjpZ/vmJTnVGiG/V7FQe/+oh0J+zW+B7WCFYkmHk7mm5thMLBuyvWckvPYOA9uli/98xbdS2ULV9fCSJK9P1w69WqnQk/VHg9+WRk9yAp9jzq8MfW4kXyjpSCq/YevhJcnrMYoEvJVNZ7VMNtlWBwAAAACARAi0ISYGLX3z7qxs29ZUufXJmPaqNYbLM4FKJfuorvw4nMqpZEtjTc0EcoKWmiFQFelcoVJJU81YzNL306foj654fXMHXmVpvf3GtmBFLb+S2cYzgc6YiG3oOQAAAAAAWweDoTfAWMxSvlDS3MKiMwS5A0OEh8J+lezGLUK9brKFjVarN601I5Ur1N06NjFotXS91ZZmPG1cJZDkDIeuVwlk23bTA7YBAAAAAJAIgTaEG3BMxjOaimc7ssJ7OByQpKN+OPQhd1taE+1UbsDRcKX7Mul8QZFg7dXt44OWZtLOnKH12Ky17FHLVzfwS2QKyiwWWQ8PAAAAAGgaIdAGcMOLyfmsDiWyTbU+NTIc8Us6+kMgtwqnmQqWmOVTOOCtvdK9inSuqAHLX/P28Zgl25amk+urBnJDoLENXBEvSQPB+iHQZCKzKecAAAAAAGwdhEAbwA047joYV6Fkd6RaYzDkVAId7cOhpxJZBXweDYdrBzUuY4zGY1ZLlUBOO1j9SiCpteqi5SbjWYUDXsWsjR2nFW3QDrZZFUkAAAAAgK2DEGgDbB8IymNU2RDWiZlAbmgyf5RXArntcc0Oyh4ftDQZzzR9/UaDoSfKs3xaqS5a7lCHBn03Eg3WHww91UJFFQAAAAAAEiHQhvB5PdoeDS6FQJ2cCZQ+yiuB4tmWWpjGBy0dSuSaum+xZGshX6y9Il5Lgdz6K4EyHQn1GhmwfErVawcrn39HlBAIAAAAANAcQqANMj4Y0vzCYvnX7X9Qj4X8MmYLVAIlWhuUPR6zdCiRVbFkN7xvOu+EJvW2g8VCPoX83nWHQIcSuU2pvolaPqXzxZq/70OJrLYNBBXw8SMMAAAAAGgOnyA3yES5WsTnMdoWCbZ9Pa/HaDDk19zC0VsJtJ615hODlgolWzOpxtVA6fIMnXqVQMYYZ038OtrBiiVbh1oMsdbLDbJqzQWa7NDWOQAAAABA/yAE2iBu0DEWs+TxdGZ+zHA4cFRvB5tN55UvllpqpxpvYYbPUghUezC05PyZrKcSaCaVU6Fkb0o7WNSqHwK12lYHAAAAAAAh0AZxQ6BOtg4NhvyKH8Xbwdwgp9V2MGlpBk49qVxRUv12MPf51xMCued3g6mNFC2vua81HLrVtjoAAAAAAJoKgYwxlxhj7jfGPGiMeWeV23/LGHOnMeZnxpgfGGPO6PxRjy4TGxACDYf9R3Ul0NJGq+ZDlFZWuruVQI1CIGfYdFalJuYMLTe5iWvZK+1gVYZDL+QLimcW2QwGAAAAAGhJwxDIGOOV9B+SLpV0hqSXVwl5PmPb9uNt2z5H0j9L+tdOH/Ro47bqdLJ1aDgcOKq3g7khSiv/TEYjAfm9pql2sFQTM4EkJwQqlGwdSTe3dczlBlGb0YbltoMlq7SDTa3jnyMAAAAAAPU/LTueJOlB27YfkiRjzOckXS7pHvcOtm0nlt0/Iqm1EostyK0W6WTVyHAkoAPzGZ3zN//X8mN/86IT9NZnnNSRc+ybXdAVH7mpso2rWdnForweo20DgaYf4/EY7Yg2177lVs00rAQqhyfPfO/35G1hXlMmX5TfazQaaf786+W2g731qtvXbAArFp0fL9rBAAAAAACtaCYE2iVp37Kv90s6f/WdjDFvlfQHkgKSLq52IWPMmyS9SZKOOeaYVs96VDlmJKy/fv6ZuuysiY5d8+VP2qNiyZZtt5axXXfXlG56aKZjIdCdB+I6MJ/R5efs1FDI39JjTx2PyedtbRTVWCyoQ80Mhs43Vwn0lJO26beedqIyLYZYknT6RKxjg77rOWFbRG9/1imarVGtNGD5dN5xwxt+DgAAAADA1tFMCNQU27b/Q9J/GGNeIenPJb22yn0+IukjkrR3794tXS1kjNFrLzyuo9c8aUdU73r+mS0/7uGZhY4OlJ4uBzJ/ftkZ2h4Nduy6teyIWnrwcKrh/VJNzgSKBH1656WndeRsG8XjMfq9Z53c7WMAAAAAALaQZkoyDkjas+zr3eXv1fI5SS9o40zosMGQX4lOhkDJnLyezWmLkqQdsWAleKonnSvIYyTLz9I7AAAAAABWa+bT8k8knWyMOd4YE5B0haRrl9/BGLO8ZOEySb/o3BHRrpjl63gItG0gsCltUZK0IxpUIltQdrFY937pXFGRoE/GbM65AAAAAAA4mjRsB7Ntu2CMeZuk/5XklfQJ27bvNsb8jaRbbdu+VtLbjDHPkrQoaU5VWsHQPbGQX4nsomzb7khAMp3MaUd084YSu891OJnTnpFwzfulcoWGrWAAAAAAAPSrpj4x27Z9naTrVn3vL5f9+vc6fC50UMzya7FoK7tYUijgbft604msdg2FOnCy5myPOXOHppPZuiFQmhAIAAAAAICaGJ7SB2IhJxhJZDvTEnY4mdOO2MYPhHbtKA+fnk5U35TlSuUKDTeDAQAAAADQrwiB+kDMcta4d2Iu0GKxpJl0Xtu70A42nWwcAlEJBAAAAABAdYRAfWAw5IRAnVgTfyTlBDE7NmE1vGs0EpDXYzSdrL8hLJ0rKBJsv90NAAAAAICtiBCoD8TKIVAn2sHclqzNDIE8HqNtA4GG7WDudjAAAAAAALAWIVAfiFnlmUCZQtvXcluydsQ2rx1MclrCaAcDAAAAAGD9CIH6QEcrgcotWZtZCeQ+X70QyLbtcjsYIRAAAAAAANUQAvWBaKUSqHPtYNsGNjkEigV1uM5MoFyhpELJphIIAAAAAIAaCIH6QNDnleX3KJHtTDvYSCSggG9zXzrbo5Zm0nkViqWqt6dzzu+NEAgAAAAAgOoIgfrEYMiv+EL7lUCHk9lNbwWTnHYw25aOpPJVb0+VQyDawQAAAAAAqI4QqE/ELH+HZgLltL1LIZDz/NVbwlKVSiBWxAMAAAAAUA0hUJ+IhToUAiVy2hHd3M1g0tI2slpr4tO5oiQqgQAAAAAAqIUQqE/ELF/bK+JLJVtHUjntiHWzEqhWCEQ7GAAAAAAA9RAC9YlOVALNLuRVKNldmQnkbiNr3A5GCAQAAAAAQDWEQH0iZvkVb3NFvNuK1Y12sIDPo5FIgEogAAAAAADWiRCoTwyG/EpkFmXb9rqv4VbhdKMdTHJawmrNBKISCAAAAACA+giB+kQs5FPJltL54rqv4VbhdKMdTJK2R4M63KAdLBJgOxgAAAAAANUQAvWJmOWXJCXaaAk7nOxeO5j7vPXawSy/Rz4vL2kAAAAAAKrhE3OfiIXKIVAbw6GnE1lFLZ9CXaq22REL6nAyp1JpbUtbKlekFQwAAAAAgDoIgfrEUiXQ+tfETydzXWsFk5w2tELJ1txCfs1t6VyBodAAAAAAANRBCNQnYiEnIGlnQ5gTAnWnFUxaakOr1hKWzhUUCRACAQAAAABQCyFQnxgMtT8TaDqZ7dpmMGlpK1m1ECiVK9AOBgAAAABAHYRAfaLSDrbOmUC2bWs60f12MMmZTbRaOl/QgEUIBAAAAABALYRAfSJaDkjWOxMokS0oVyj1bDtYKstMIAAAAAAA6iEE6hM+r0eRgHfdlUCHk071TTfbwUIBr6JBX2VV/XLOdrDubC0DAAAAAOBoQAjUR2Ih/7pnAk0nnOBlexfbwSRpeyyo6WSVdjAGQwMAAAAAUBchUB+JWf51bwdzW7C62Q7mPH+wEki5iiVbmcUi7WAAAAAAANRBCNRHBkP+dbeDTfdAO5jkhFCrZwKl886cI7aDAQAAAABQGyFQH4mFfOseDD2dyMnyexTtctCyI+q0g9m2XfleOuf8nqgEAgAAAACgNkKgPhKz2qkEymlH1JIxpsOnas2OWFDZxZKSuaUwaykEYjA0AAAAAAC1EAL1kbYGQyez2tHlodDSsjXxy+YCpXJFSVLUohIIAAAAAIBaCIH6SMzyKZkrqFSyG995lelkruvzgCRVgqjlG8JS2XIlENvBAAAAAACoiRCoj8RCftm2VrRSNetwItf1zWDS0mDqw8nllUDMBAIAAAAAoBFCoD4SC/klqeWWsEy+qGSuoO090A62vUo7mDsTiO1gAAAAAADURgjUR2JWOQRqcTh0ZT18D4RAMcsny+9Z0Q7mroinEggAAAAAgNoIgfpILOSEJK2uiZ8ut17tiHW/HcwYox1Rq3ImaakdjEogAAAAAABqIwTqI+uuBCq3XvVCJZDknGN1O5jHSJaflzMAAAAAALXwqbmPDK5zJlAvtYNJznDoFe1guaIGgj4ZY7p4KgAAAAAAehshUB9xB0PHWw6BcvJ5jIbDgY04VstWt4MlswVawQAAAAAAaIAQqI9Egz4ZIyWyLc4ESuS0PRqUx9MblTb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      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize = (20,20))\n",
    "plt.plot(gps_distances, label = 'gps distance')\n",
    "plt.plot(imu_distances, label = 'imu distance')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "prev_imu_data = None\n",
    "locations = []\n",
    "for frame in range(150):\n",
    "    imu_data = read_imu('/home/udi-kin/Downloads/kin_dataset/2011_09_26/2011_09_26_drive_0005_sync/oxts/data/%010d.txt'%frame)\n",
    "    \n",
    "    if prev_imu_data is not None:\n",
    "        displacement = 0.1*np.linalg.norm(imu_data[['vf','vl']])\n",
    "        yaw_change = float(imu_data.yaw - prev_imu_data.yaw)\n",
    "        for i in range(len(locations)):\n",
    "            x0,y0 = locations[i]\n",
    "            x1 = x0 * np.cos(yaw_change) + y0* np.sin(yaw_change) - displacement\n",
    "            y1 = -x0 * np.sin(yaw_change) + y0* np.cos(yaw_change)\n",
    "            locations[i] = np.array([x1,y1])\n",
    "    locations += [np.array([0,0])]\n",
    "    prev_imu_data = imu_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([-55.24572559, -24.47145956]),\n",
       " array([-54.91022885, -24.37565838]),\n",
       " array([-54.57791154, -24.2762731 ]),\n",
       " array([-54.25259246, -24.17455335]),\n",
       " array([-53.93467008, -24.07025329]),\n",
       " array([-53.62178149, -23.9632472 ]),\n",
       " array([-53.31267038, -23.85318867]),\n",
       " array([-53.00965276, -23.7407106 ]),\n",
       " array([-52.71024957, -23.62560908]),\n",
       " array([-52.41393101, -23.50778036]),\n",
       " array([-52.12104176, -23.38704771]),\n",
       " array([-51.83003203, -23.26301029]),\n",
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